rolex yacht master 42mm

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OYSTER PERPETUAL YACHT-MASTER 42

MASTERING LIGHTNESS

Light and robust, the new Yacht-Master 42, in RLX titanium, is the ally of those who revel in freedom. Especially suited to the demands and pressures of competitive sailing, it delivers exceptional performance.

Rolex is presenting a new version of the Oyster Perpetual Yacht-Master 42. This nautical watch is introduced for the first time in RLX titanium – a particularly strong but lightweight alloy – and is fitted on an Oyster bracelet. The new version stands out for its technical satin finish – a satin finish with a visible grain, a feature of Rolex watches in RLX titanium – which extends to the middle case sides, the edges of the bracelet links and the sides of the clasp cover. The chamfered top edges of the middle case lugs have a high-sheen finish, while the crown guard is polished. With its bidirectional rotatable bezel fitted with a Cerachrom insert in matt black ceramic featuring raised and polished numerals and graduations, the Yacht-Master 42 in RLX titanium remains faithful to the aesthetics of the original model, unveiled in 2019. It is graced with an intense black dial in a fine satin finish, and its Oyster bracelet is equipped with the Easylink comfort extension link. The new version of the Yacht-Master 42 is equipped with calibre 3235, a movement at the forefront of watchmaking technology, enabling it to display the date as well as the hours, minutes and seconds. Like all Rolex watches, the Oyster Perpetual Yacht-Master 42 carries the Superlative Chronometer certification, which ensures excellent performance on the wrist.

THE CALL OF THE OPEN SEAS Launched in 1992, the Yacht-Master was designed specifically for navigators and skippers. Embodying the rich heritage that has bound Rolex and the world of sailing since the 1950s, this Professional-category watch provides a perfect blend of functionality and nautical style, making it equally at home on and off the water. An emblematic nautical timepiece, it is easily recognized by its bidirectional rotatable 60-minute graduated bezel made entirely from precious metal or fitted with a Cerachrom insert in high-technology ceramic.

RLX TITANIUM  RLX titanium is a grade 5 titanium alloy specially selected by Rolex. Like all titanium alloys, it is especially lightweight and is noted for its mechanical strength and corrosion resistance. Another characteristic of RLX titanium is the possibility of working it to give a polished or satin finish according to the brand’s specifications. Its high mechanical strength makes it complex to work with, and the decision to use it has required the introduction of special production processes.

HIGH-TECHNOLOGY CERAMIC Rolex played a pioneering role in the development of special ceramics for creating monobloc bezels and bezel inserts. Not only are these materials virtually scratchproof, their colours are also of a rare intensity and are resistant to environmental effects. In addition, thanks to its chemical composition, the high-tech ceramic is inert and cannot corrode. Rolex has developed exclusive expertise and innovative manufacturing methods that grant it complete independence in the production of these ceramic components. On the new version of the Yacht-Master 42, the bidirectional rotatable bezel is fitted with a 60-minute graduated Cerachrom insert in matt black ceramic. Its raised graduations and numerals are first moulded into the ceramic and then polished. The first 15 minutes are graduated minute-by-minute to allow time intervals to be read with great precision. The bezel can also be turned with ease thanks to its knurled edge, which offers excellent grip.

OYSTER CASE, SYMBOL OF WATERPROOFNESS  A paragon of robustness and reliability, the 42 mm Oyster case of the new Yacht-Master 42 is guaranteed waterproof to a depth of 100 metres (330 feet). The middle case is crafted from a solid block of RLX titanium. Its case back, edged with fine fluting, is hermetically screwed down with a special tool that allows only Rolex watchmakers to access the movement. The Triplock winding crown, fitted with a triple waterproofness system and protected by an integral crown guard, screws down securely against the case. The crystal, which features a Cyclops lens at 3 o’clock for easy reading of the date, is made of virtually scratchproof sapphire and benefits from an anti-reflective coating. The waterproof Oyster case provides optimal protection for the movement it houses. 

PERPETUAL CALIBRE 3235  The new version of the Yacht-Master 42 is equipped with calibre 3235, a movement entirely developed and manufactured by Rolex that was released in 2015 and has been fitted on this model since its launch in 2019. A distillation of technology, this self-winding mechanical movement delivers outstanding performance in terms of precision, power reserve, convenience and reliability.   Calibre 3235 incorporates the patented Chronergy escapement, which combines high energy efficiency with great dependability. Made of nickel-phosphorus, this escapement is resistant to strong magnetic fields. The movement is fitted with a blue Parachrom hairspring, manufactured by Rolex in a paramagnetic alloy. The hairspring offers great stability in the face of temperature variations as well as high resistance to shocks. It is equipped with a Rolex overcoil, ensuring the calibre’s regularity in any position. The oscillator is mounted on the Rolex-designed, patented high-performance Paraflex shock absorbers, increasing the movement’s shock resistance. The oscillating weight is now fitted with an optimized ball bearing. Calibre 3235 is equipped with a self-winding system via a Perpetual rotor. Thanks to its barrel architecture and the escapement’s superior efficiency, the power reserve of calibre 3235 extends to approximately 70 hours. 

OYSTER BRACELET AND OYSTERLOCK SAFETY CLASP  The new version of the Yacht-Master 42, made from RLX titanium, is fitted on an Oyster bracelet. Developed at the end of the 1930s, this three-piece link bracelet remains the most universal in the Oyster Perpetual collection and is known for its robustness. The Oyster bracelet of this new version of the Yacht-Master 42 features the Oysterlock folding safety clasp, which prevents accidental opening. It is also equipped with the Easylink comfort extension link, developed by Rolex, which allows the wearer to easily adjust the bracelet length by approximately 5 mm. The Oyster bracelet in RLX titanium also includes patented ceramic inserts – designed by the brand – inside the links to enhance its flexibility on the wrist and its longevity.

SUPERLATIVE CHRONOMETER CERTIFICATION Like all Rolex watches, the Oyster Perpetual Yacht-Master 42 is covered by the Superlative Chronometer certification redefined by Rolex in 2015. This designation testifies that every watch leaving the brand’s workshops has successfully undergone a series of tests conducted by Rolex in its own laboratories according to its own criteria, following the official certification of the movements by the Swiss Official Chronometer Testing Institute (COSC). The in-house certification tests apply to the fully assembled watch, after casing the movement, guaranteeing superlative performance on the wrist in terms of precision, power reserve, waterproofness and self-winding. The precision of a Rolex Superlative Chronometer is of the order of −2 /+2 seconds per day – the rate deviation tolerated by the brand for a finished watch is significantly smaller than that accepted by COSC for official certification of the movement alone. The Superlative Chronometer status is symbolized by the green seal that comes with every Rolex watch and is coupled with an international five-year guarantee.

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The stealthy excellence of the Rolex Yacht-Master 42 in white gold (ref. 226659)

If you weren’t paying close attention you could easily miss the new Rolex Yacht-Master 42 in white gold. And I mean that in the best way. It’s a handsome timepiece, with a quiet presence that (despite its newness) feels reassuringly familiar in some hard-to-define way.

Despite being 2mm bigger than its Everose Gold brother (and a 42mm Oyster case wears quite big), it’s the opposite of in your face. In fact, thanks to the monochrome colour scheme of the dial, bezel and case, and the matt black Oysterflex strap, it may be the most discreet model in Rolex’s entire line-up.

I’m not suggesting that it can’t be a love-at-first-sight piece – but the more closely you look, the greater the rewards. The absence of colour draws your eye to the details, and emphasises the play of shiny and matt surfaces: the fine band of polished notches around the outer edge of the bezel contrasts with the matt Cerachrom insert, which in turn plays against the shine of the black lacquer dial. The polished surfaces of the raised numerals on the bezel (they are an integral part of the bezel, moulded with the ceramic, rather than applied) cast barely-there shadows on the surrounding matt surface. And when the light plays over the highly polished case-side, with its seamless cutaway from lugs to crown guards – well, it’s a beautiful thing. The curve is perfect and the white gold has a visual softness that you just don’t get with steel or platinum.

The matt black rubber Oysterflex strap is the Goldilocks element, tying everything together in just-right harmony (whereas the hardness and shine of a metal bracelet could throw things off-balance visually).

Having been introduced in 2015, the Oysterflex strap is no longer news but it’s worth revisiting because it’s such a great piece of design – possibly the most comfortable watch strap known to mankind. That’s thanks to the tiny, flexible ‘blades’ hidden on the underside between a pair of longitudinal cushions. The effect is a feeling of extraordinary lightness on the wrist (balanced in this case by the pleasant heft of the white gold case), absolute stability and … no sweat. Literally, since the air can circulate between skin and strap. The folding clasp has an integrated extension system that enables you to adjust it in 2.5mm increments, up to a total of about 15mm – instantly and without tools.

On the dial, simplicity of the markers and their assertive size make it highly legible (again, with no colour to distract the eye – not even the line of red text that appears on other Yacht-Masters). Thanks to the Chromalight lume (which glows blue in the dark) none of this clarity is lost at night.

Rolex scores more points by bringing its new-generation Calibre 3235 (launched last year) into the Yacht-Master range for the first time – a technical step up from the Everose Gold model. The key attributes of cal.3235: the patented Chronergy escapement, designed to maximise energy efficiency and made of magnetically neutral nickel-phosphorus, and the Parachrom hairspring, resistant to shocks and also paramagnetic. The benefit: accuracy of +2/-2 seconds per day (tested after casing), more than twice the standard chronometer specification.

As we expect from Rolex, this is an exceedingly well built and tremendously practical watch, ideally suited to everyday sporting wear. Although it’s water-resistant to 100 metres, it’s designed for above-water not underwater life. The bezel is bi-directional; it’s Yacht-Master, not Dive-Master. It’s not a tool watch. So while I briefly thought – as I’m sure many of you did – “Why not steel?” I no longer ask that (rhetorical) question. White gold is entirely appropriate and, in this handsomely sporty monochrome package, it’s the stealth Rolex par excellence.

Rolex Yacht-Master 42 (ref. 226659) price

Rolex Yacht-Master 42 , white gold on Oysterflex, $36,950

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Rolex Yacht-Master 42 Oyster, 42 mm, RLX titanium M226627-0001

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All Rolex watches are assembled by hand with the utmost care to ensure exceptional quality. Such high standards naturally restrict Rolex production capacity and, at times, the demand for Rolex watches outpaces this capacity.

Therefore, the availability of certain models may be limited. New Rolex watches are exclusively sold by Official Rolex Jewelers, who receive regular deliveries and independently manage the allocation and sales of watches to customers.

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Light and robust, the new Oyster Perpetual Yacht-Master 42 in RLX titanium is the ally of those seeking freedom of movement. Especially suited to the demands and pressures of competitive sailing, it puts watchmaking excellence at the service of sporting performance.   The Yacht-Master 42 is the second watch in RLX titanium released by Rolex after the Oyster Perpetual Deepsea Challenge, confirming that lightness is a quality to be taken seriously..

ROLEX YACHT-MASTER

MASTERING LIGHTNESS

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Terrific in Titanium: The Rolex Yacht-Master 42 Ref. 226627 

Published by samuel colchamiro.

View all posts by Samuel Colchamiro

Rolex doesn’t have much of a reputation for innovation. While many brands push modern designs and flashy novelties, the crown is the master of incremental changes, tweaking its designs every few years in a slow, continuous act of refinement. That said, when Rolex does do something out of the ordinary, as was the case with the new Yacht-Master 42 reference 226627 , it really makes a splash. At first glance just another run of the mill Rolex sport watch, this 42mm 2023 release added the second all- titanium model to the Rolex collection. Let’s take a closer look!

The Rolex Yacht-Master debuted in 1992 as something of a modernized Submariner. Believed to originally have been intended as merely an update to the Submariner, it quickly became clear that the Yacht-Master deserved to be its own model with its own distinct personality. A luxurious, precious metal variant of the classic Submariner tool watch, the Yacht-Master had a unique identity and quickly became a popular staple in the collection. The updated look featured an insert that was permanently affixed to the bezel (as opposed to the floating insert found on the Submariner), the handset was wider, the dial had maxi style indexes, and of course, precious metal was standard.

Since then, the Yacht-Master collection has expanded to include a number of additional references. In 1994, Rolex added a midsize 35mm variant as well as a lady’s 29mm piece. In 2015, Rolex added an Everose gold model paired with an Oysterflex rubber strap, and in 2019, Rolex debuted the modern Yacht-Master, resized to 42mm with an Oysterflex strap. However today, we are talking about the most recent news in the Yacht-Master department, the 42mm titanium Yacht-Master that made its debut in 2023. As its first mass-market titanium watch (yes the Deepsea Challenge came first, but that was a super limited niche piece), the watch was a real surprise coming from the normally conservative brand. 

Design Details

Beyond the case and bracelet material, this Yacht-Master was designed to remind you of the precious metal Yacht-Masters that Rolex has produced the last few years. This watch features the maxi dial, rounded edges, and overall luxurious feel that the model is known for, and yet, it is a very different animal. The matte look of the brushed titanium paired with a matte black ceramic bezel, black dial, and white lume plots fuse to create a stealth presence on the wrist. The more muted tones of the bezel compared with, say, a Submariner, really set this piece apart. 

While the aesthetics help set the stage for this watch, the feel on the wrist tells a slightly different story. Whereas a 42mm Rolex should be a substantial piece on the wrist, this titanium variant weighs a mere 100 grams – which is a blessing or a curse depending on who you ask. If you’re looking for the reassuring heft of a luxury sports watch, this may not be for you. If, on the other hand, you find a heavy watch imposing or uncomfortable, there is finally an option for you from a brand that hasn’t historically offered much in the way of lightweight pieces (sure, Rolex has the Deepsea Challenge, but few wrists are capable of wielding that thing). 

The bracelet on this watch is the classic Oyster style that we have come to know and love from Rolex, meaning that there’s nothing particularly aggressive or polarizing about the execution. If appreciate titanium, you are going to like this watch quite a bit.

Alongside the other recent releases from Rolex (a Daytona with an exhibition caseback, puzzle and emoji dial Day-Dates, bright colored OPs, etc), a titanium model actually seems fairly subdued. Maybe this is because, as an industry, we are adjusting to a more adventurous Rolex than we were historically used to. Interesting to imagine what else might be in the pipeline…

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Inner Workings

The ref. 226627 Yacht-Master is powered by Rolex’s automatic-winding caliber 3235 movement. The 3235 was introduced in 2015 as Rolex’s updated automatic winding movement with a date and came with an elongated 70-hour power reserve alongside other improvements including a longer mainspring and thinner barrel walls. The movement is accurate within +/- 2 seconds per day and is generally regarded as one of the most robust and easiest to service in the industry. The movement is much higher tech than previous calibers from Rolex and came as a welcome upgrade that brought Rolex watches in line with the standard longer power reserve that is now the norm in the industry. 

Versus the Competition 

There are a number of alternatives to the Titanium Yacht-Master that you should consider if you’re in the market. While the Yacht-Master was originally intended to be a more luxurious model from Rolex, in titanium, it has a much sportier presence. Those seeking the more luxurious feel would likely gravitate towards one of the precious metal variants, like any of the the yellow, rose, or white gold 42mm Yacht-Master’s Rolex offers. Additionally, the brand has a number of special exotic dial variants, including a tiger’s eye Yacht Master that is worth a look. 

If you are searching for a more affordable titanium diver, the Tudor Pelagos 39 is quite difficult to beat. For a fraction of the price, you gain access to the lightweight tool watch club. The Pelagos is one of Tudor’s most popular watches today and brings all the capabilities of a hardcore diver without breaking the bank. 

Finally, I’d recommend the hardest core diver that Rolex has produced in a long time, the Deepsea Challenge . Also fashioned from titanium, this monstrous piece supplies a depth rating of not one meter less than 11,000. There’s something reassuring about knowing that you will die long before your watch will. The Deepsea Challenge was Rolex’s first series-produced titanium watch and, coupled with its impressive specs, it makes for a really unique piece that pays tribute to the brand’s origins as a maker of bulletproof tool watches.

Personality

I picture the Ref. 226627 as a weekend warrior. Titanium is a relatively soft material, but the natural oxidation of the surface over time makes small scratches essentially disappear. This unique quality offers a degree of comfort that you don’t necessarily have with other, “shinier” Rolex models. Additionally, this piece has a casual, non-ostentatious appearance to it. The bezel is matte finished rather than high gloss ceramic; the titanium has a robust, utilitarian gray hue to it that stainless steel lacks; the maxi markers look stronger and less delicate. These aesthetic choices add up to a look and feel that is reassuring on the wrist. There’s little concern for how the piece will hold up and it’s clearly a watch that you can put on and forget about. 

Final Thoughts

The ref. 226627 Rolex Yacht-Master is many things. On the one hand, it’s part of the brand’s luxurious diver model family. On the other hand, it’s a titanium diver that packs a 100 meter water resistance and a James Bond, Jason Bourne-esque persona. Rolex as a brand is truly the master of logical paradoxes (luxury tool watches anyone?). A purpose built tool that is constructed with refinement and luxury as core priorities shouldn’t work. And yet, as Rolex has shown time and time again, it does, and effortlessly so. The Rolex Yacht-Master in titanium is just the latest in the brand’s longstanding tradition for crafting luxurious tool watches that deserve a place in your watch roll. 

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Hands-On: Rolex Yacht-Master 42 Watch Reference 226627 In RLX Titanium

The visceral experience of wearing the Yacht-Master 42 titanium is very odd for any long-time Rolex fan. Rolex more or less helped create the 20th-century notion that you can often measure the value of a watch by feeling how solid and weighty it is. Rolex watches have never been designed for lightness, so most of them are quite hefty, and beloved for that reason. It is common for someone to admire a precious metal Rolex simply by feeling its mass in the open palm of your hand.

The Rolex Yacht-Master 42 was introduced in 2019 with tones very similar to this titanium model, but rather in 18k white gold and on a black Rolex Oysterflex strap. A yellow gold version was eventually added, and it seemed as though Rolex’s largest Yacht-Master was destined to be a precious-metal-only product. The 2023 226627 Yacht-Master 42 changes that paradigm by adding in a full grade 5 titanium case and matching bracelet to the product family. This is what people should consider the first “wearable” Rolex watch produced from titanium.

Even though Rolex uses the same grade 5 titanium as other brands, it focuses a lot on surface finishing and polishing for this timepiece. Rolex uses a sort of deep-grain engraving, which is somewhat different from the same effect on steel. Titanium as a color is also a bit darker than the comparatively bright 904L steel that most other (non-precious metal) Rolex sport watches are made out of. Titanium does scratch, and I asked Rolex about the service plan for the Yacht-Master 42 in RLX titanium. To make a long story short, Rolex will offer the same “case refresh” service for its titanium watches as it does for its steel and gold watches, though in reality, Rolex will have to use some special processes to polish titanium so that it looks fresh and new again. “RLX titanium” is really just Rolex’s way of indicating that it polishes and finishes titanium metal differently from other brands (according to Rolex).

Other than being in titanium with the matching bracelet, there isn’t too much new here. The Yacht-Master 42 case is 42mm-wide and has similar proportions as other watches in the larger Oyster Perpetual watch. The case is water resistant to 100 meters, and around the dial is a uni-directional rotating bezel with a matte-black ceramic insert that matches the matte-dark-gray tone of the Yacht-Master 42 dial.

Inside the watch is the Rolex in-house-made caliber 3235 automatic movement that operates at 4Hz with about 70 hours of power reserve. The movement offers the time with date and on the sapphire crystal is a Rolex “cyclops” magnifier lens. Titanium is considered by many engineers to be the perfect material for wristwatch cases. While I don’t think it is possible to ever determine “bests” in regard to an emotional product, it is true that you can easily enjoy the Yacht-Master 42 in titanium from purely a tool watch perspective. The lighter weight and large size give this 42mm-wide Rolex an interesting and desirable personality. It also makes us wonder whether or not there will be more titanium Rolex watches in the future. Possibly some, but I don’t think that Rolex, primarily a maker of conspicuous jewelry-style watches, will heavily focus on a material that will not hold a high polish as nicely as steel, gold, or platinum watches.

For watch lovers and Rolex collectors, there really is a lot of novelty to wearing a Rolex watch in titanium simply because most people haven’t ever done so before. The sister brand Tudor has had the Pelagos, which, for a while, was really the more sober equivalent of this Rolex Yacht-Master 42 226627. It is also much less expensive, but it doesn’t have the iconic Oyster Perpetual case shape and the famous Submariner-style dial that this Yacht-Master does.

While the Yacht-Master 42 in RLX titanium raises a lot of interesting philosophical questions about what Rolex should and shouldn’t be doing, the product will be a commercial success given the current latent demand for high-end titanium sport watches and anything even remotely interesting from Rolex. Rolex has made it clear that production of the titanium Yacht-Master 42 is going to be limited in scope, in large part because there are so many pieces of titanium in the case, and especially the bracelet, that all need to bear precisely matching polishes and finishes. I don’t imagine that this watch is easy for Rolex to make, but we do know that Rolex could increase production of titanium watches if it ever wanted to. Price for the very interesting and comfortable reference 226627 Rolex Yacht-Master 42 in RLX titanium is $14,050 USD . Learn more at the Rolex watches website here .

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Why the Rolex Yacht-Master 42 Is the Perfect Poster Boy for a New Age

The watch industry is always changing, always evolving, always reacting to the conditions of the world. But some things seem eternal. Through good times and bad, one brand has, since its foundation in 1905, seemed immune to weakness. A history of constant innovation and textbook PR has resulted in a Rolex watch becoming one of the most desirable products in the world, and the brand name that is easily one of the most recognizable.

To establish the kind of reputation Rolex has, you have to do things differently. You also have to be ahead of the curve. Rolex has done that with aplomb in the past, and by carefully controlling the supply of steel watches, while moving the brand forward in a slightly different direction through the release of some headline-grabbing pieces at this year’s Baselworld (including the new Rolex Yacht-Master 42), Rolex is continuing to map its own destiny.

It didn’t begin with the renaissance of Tudor, but that event in 2007 now looks in retrospect to be the beginning of a transformation that elevated Rolex from a brand to some kind of looming deity. Simply put, Tudor is the new Rolex, and Rolex is a new thing entirely. When I speak to brand managers the world over (many of whom are trembling and sleep-deprived as the struggle to conceive strategies to lever attention away from the Crown) they all say the same thing in one way or another.

Rolex is no longer a brand; Rolex is a phenomenon. Trying to go toe-to-toe with Rolex is like boxing with your reflection, or shooting at smoke. It’s a fight you can’t win. It isn’t about the fact your opponent is stronger than you, it’s about the fact that your opponent is fighting on a different plane of existence entirely.

For three years I managed the sales accounts of a well-known German brand across 17 countries. Despite the vastly differing cultures, tastes, and consumer motivations, the presence and adulation of Rolex were ever-present. The power of the Crown is never more felt than when trying to negotiate space in a retail environment for the product of another brand.

For example, I once asked a retailer if I could commandeer thirty centimeters of unused counter space in his store. His face screwed up instantly as if suddenly overtaken by a crippling bout of indigestion. “Well,” he said through gritted teeth, “I’ll have to check with Rolex.”

Duly, he made the call. The answer came back as predicted.

“No,” he said, unable to make eye-contact with me. “Rolex wants that space now.”

I had offered him a full collection of watches with an extended payment terms so he could get the models into his store and test the water before making a significant financial investment. In contrast, Rolex had offered him one GMT Master II “Batman” (on the professional bracelet, not the Jubilee). The choice, for him, was simple. The “Batman” was already sold. It was money in the bank. And his relationship with Rolex – the lifeblood of his livelihood – was intact.

Trying to build a brand next to Rolex is like growing a tree in the shadow of a mountain. It’s not impossible, but there isn’t much light left to go around.

That kind of power is hard to contend with. Rolex has cultivated it over generations. One way in which they’ve managed this is to have been a tireless inventor. The brand’s watches have always been innovative and eminently practical. With every stone turned over in search of incremental gains, Rolex even changed the type of steel it uses in the production of its watches because it could.

Rolex uses 904L, while most of the industry uses 316L stainless steel. Most notable is 904L’s higher molybdenum content, which reduces the chance that the steel will pit or corrode. Although this kind of benefit is the sort of thing you might imagine other brands trying to cash in on, it isn’t that simple. 904L stainless steel is notoriously difficult to work with. Not only do you need special tools to cut it, you also need to be patient as it has an annoying tendency to overheat while being machined. This means manufacturers using 904L must be patient to avoid it blowing up (almost literally) in their faces.

Despite its foibles, Rolex’s first use of 904L steel was in a 1988 Sea-Dweller, and its collection-wide adoption of it in 2003 has been one of the brand’s calling cards. It makes perfect sense for a rugged sports watch brand that is hell-bent on improving the performance of its products. But the thing is, Rolex is changing. Rolex, for all its insistence that it is still a brand built around endeavor and adventure, is transitioning into something else, and its special steel (Oystersteel as they’ve started calling it since 2018) is no longer as central to the brand’s image as it once was.

Around the time of the Tudor rebrand (2007), you could still buy a Rolex no-date Submariner for around four and a half grand. Today the price is double that. Even adjusting for inflation won’t make you feel much better about not loading up on Submariners when you had the chance to get them for a relative song.

But crazier than the current retail prices is the current availability. In 2007, you could walk into a store and buy a green dial, green bezel Rolex Submariner “Hulk” out of the cabinet. If you had the money, you could get the watch. Waiting lists, enforced scarcity, and secondary market prices double the ticket just didn’t exist like they do today. Now, finding a retailer that has a professional model in stock is harder than finding a hen with teeth.

Tudor, meanwhile, has remained very fairly priced. With all the benefits of Rolex’s know-how and freedom to reinvent the brand, Tudor has carved its own niche with its own audience. It is, almost without a doubt, the Rolex of its generation.

Its savvy marketing campaign, excellent build quality, and never-overstated connection to the industry’s Big Brother has made it a hit with the customers discovering the industry for the first time. The Pelagos and Black Bay watches of today will likely be future classics in the way the Submariners and the Datejusts of old are today.

But since Rolex became an industry super-power, removed from the regular concerns of watch companies, it needed a new champion, a poster boy for a new age, a darling that is at once everything Rolex was and everything it is going to be…

The Rolex Yacht-Master 42

The stage could not have been more perfectly set for the release of the Rolex Yacht-Master 42 ref. 226659 at this year’s Baselworld. With its white gold case, simple black dial, matte black Cerachrom bezel, and black Oysterflex rubber strap, it is the epitome of stealth luxury. It also happens to be just about the most hypnotic black/black diver on a rubber band that anyone could imagine.

Why is the Yacht-Master 42 so perfect for this new age? Well, its a barrel load of contradictions packed into a 42mm case. But those contradictions are what make Rolex the brand it is these days. They are what makes it so hard to copy what the brand does. Because, on paper, it just doesn’t add up.

For all intents and purposes, the Yacht-Master 42 is a sports watch. And yet it’s not. Not really – its white gold case puts paid to that idea before the conversation even begins. It is instead, perhaps the most luxurious, desirable, deliciously devil-may-care thing the brand has ever produced.

Within the 18k white gold case of the Yacht-Master 42 is the Rolex Caliber 3235. The recipient of 14 patents, it includes the new Chronergy escapement, which is highly efficient and dependable. It contains all the hallmarks of a Rolex Perpetual movement suited to an active lifestyle, such as the Blue Parachrom hairspring (highly resistant to magnetic fields and temperature interference) and the Paraflex shock absorption system.

When you think of crazily luxurious watches made by Rolex, you’d be forgiven for thinking of a diamond-encrusted case, or even a rainbow bezel. But this trumps them all. The Yacht-Master 42 is the kind of luxury that only people in the know understand. For your investment, you don’t get anything that will scream your wealth across a room. Instead, you get something that is deliberately pared back. Something that is designed to catch the corner of an onlooker’s eye, but not their full attention. The Yacht-Master 42 is a watch that demands awareness to be appreciated. This is something for the wearer themselves to enjoy. Something deeply personal. Something deeply Rolex.

There are many stories as to where the five-pronged logo of Rolex comes from, and even more about what it represents. But if we take it for what it simply appears to be, it could not be more fitting. This brand is king. It may not have the finest movement finishing, the most mind-boggling complications, or price tags on a level with mansions or private jets, but Rolex has a heritage, a character, and a reputation that cannot be usurped. With this new direction with the Yacht-Master 42 carving out an evermore inaccessible niche, the brand next to whom any other would be happy to sit continues to stride further and further away from the pack.

About Rob Nudds

Rob Nudds is a WOSTEP-trained watchmaker, who graduated from the British School of Watchmaking. After working at the bench with brands such as Omega, Longines, Blancpain, and Bremont, he began working for NOMOS Glashütte, managing a retail network covering 17 countries, most notably the UK and USA.

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The Rolex Yacht-Master 42 RLX Titanium, Very Rolex Yet Surprisingly Disturbing

It is undoubtedly a rolex from head to toe, yet it felt very surprising on the wrist....

Launched in 1992, the Rolex Yacht-Master has undoubtedly enjoyed a long shelf life but has never attained the same cult status as a Submariner. A watch inspired by the nautical world and meant to be used as a luxury yachting watch, its vocation and looks have always been slightly confusing. It looks somewhat like a Submariner but without the diving credentials. It has sporty specifications, yet it is truly luxurious and has often appeared in precious metals. Recent versions, with the enlarged 42mm diameter and the matte black bezel, changed this perception. But clearly, it’s the new Yacht-Master 42 RLX Titanium that signs the independence act of the collection. We’re taking a closer look at this watch that feels extremely familiar and, at the same time, left us with contradictory thoughts. Good or not, let’s check this out. 

Some context

The Yacht-Master collection was introduced at the 1992 edition of the Baselworld Fair. And believe me, this was quite an event. Rolex is known for its strategy of incremental updates and rarely presents new watches. In fact, when the YM was launched, it was one of the very few entirely new collections since the launch of the Daytona in 1963. However, despite the new name (well, not entirely, as it was first used in the late 1960s on a prototype watch based on a Daytona) and its unprecedented vocation within the Rolex portfolio, the watch felt familiar.

The modern-day version of the Yacht-Master made its first appearance with the reference 16628, an 18k yellow gold version with a white dial and black-filled hour markers. Looking at it, the resemblance with the Submariner and other aquatic models at Rolex is… obvious. Some say (nothing official here) that during the 1980s, the brand experimented with many different options to revamp its iconic Submariner. Several attempts later, something close to the Yacht-Master appeared; however, Rolex felt that redesigning the Sub was probably not the right move. Yet, the design caught people’s attention but had to be slightly updated so as not to cannibalise the all-important Submariner. The decision was made to position this design as a higher-end, luxurious nautical model.

The differences were straightforward: steel, 300m water-resistant case, black bezel, black dial and instrumental characteristics for the Submariner compared to the gold; 100m water-resistant case, bright dial, solid gold bezel, more rounded shapes and luxurious touches for the Yacht-Master. However, the visual resemblance persisted, which is probably why it took many years for the YM to become a model on its own. An important moment in the history of this watch is, to me, the introduction of the reference 116655 , an Everose model with a matte black dial, a matte ceramic bezel with raised numerals and the Oysterflex rubber bracelet. This is the moment when the YM became different… and much more attractive.

Since then, Rolex has gradually improved its nautical watch with the release of the Yacht-Master 42 – a new size, larger than a Submariner, to differentiate the collection. It would later be followed by a very appealing yellow gold edition .

The Prototype Yacht-Master 42 of Sir Ben Ainslie… In titanium

The current RLX Titanium edition of the Yacht-Master 42 doesn’t spring out of nowhere. It has existed for about three years already, but only as a prototype, on the wrist of legendary sailor Sir Ben Ainslie – winner of the 34th America’s Cup with Oracle Team USA in 2013, four-time Olympic champion, CEO and Skipper of INEOS Britannia and skipper of the Great Britain SailGP Team. This watch came to us as a surprise long after its creation in 2020. Despite appearing in the wild repeatedly, we only noticed its existence in late 2021, after it was mentioned in an interview on October 2020 in The Week . It was also mentioned in the official Rolex print magazine.

The watch in question, a prototype made exclusively for Ainslie for a very specific purpose and designed to be tested on the field, was made in RLX Titanium (back then an unprecedented material for Rolex, which would later be used on the Deepsea Challenge ) and looked like a deluxurised version of a well-known watch. Entirely matte, equipped with a black dial and black bezel and with a no-date display, it was worn on a technical NATO strap, which according to the brand, combines Cordura with high-performance elastomer and is closed by a Velcro for easy adjustments.

The existence of this watch immediately gave us some ideas, incorporated in our 2022 Rolex Predictions featuring a titanium Yacht-Master . And as we anticipated, it became a reality this year .

The Rolex Yacht-Master 42 RLX Titanium

This year, Rolex launched its commercial version of the Yacht-Master 42 Titanium, which resulted in a slightly different watch from what we’ve seen on the wrist of Ainslie. More in line with the current white gold and yellow gold YM42 , the watch has many distinctive features. It isn’t just a titanium attire; it is a standalone model with its specificities and unique features.

First of all, let’s talk titanium at Rolex. The brand, over the years, has been using an array of metals – steel, with its own Oystersteel alloy (904L), gold in all possible colours and even proprietary alloys and platinum. Ceramic has long been used too, but only for bezel inserts. Rolex has never used ceramic or any other high-tech material for its cases. Until the recently introduced Deepsea Challenge , titanium has remained a rarity, used for the caseback of the Sea-Dweller Deepsea and for the Pelagos , which isn’t a Rolex but a Tudor, so it doesn’t really count.

Now, in less than six months, Rolex has released two watches made entirely of titanium, with an alloy named RLX – which is grade 5 titanium. One is a beast, a gigantic timepiece made to explore the deepest point of the oceans – a watch, objectively speaking, that is hardly wearable. The other one, the Yacht-Master 42 RLX Titanium reference 226627, is certainly one of the most comfortable models in the brand’s collection.

As said, the YM42 Titanium is more than just a new material applied to an existing watch. Of course, it shares multiple elements with its gold siblings, but some details truly set it apart. The case measures 42mm in diameter with a fairly contained 11.60mm thickness. Measuring 50.3mm from lug-to-lug, it’s not the smallest watch in the brand’s portfolio and wears slightly larger than a classic Submariner (40.5mm x 12.5mm x 47.6mm). All parts of the habillage are made of titanium, from the monobloc middle case to the crown, the rotating bezel, the caseback and the bracelet. The specifications are classic Rolex Yacht-Master, with a Triplock crown with integral guards, a screwed back, a sapphire crystal with AR coating and a Cyclops lens over the date and 100m water-resistance.

Classic features of the YM have been retained, such as the bidirectional bezel with a 60-minute Cerachrom insert. The latter sticks to the classic look of the collection, with a matte base and raised, polished numerals and markers without a lumed index. What makes the Yacht-Master 42 RLX Titanium so special, then? Well, first of all, the case features one very appealing detail: bevelled lugs. A nod to the past, this feature is exclusive to this titanium version and brings more dynamism to the case, as well as providing a nice historical reference – something that Rolex fanboys will surely appreciate (I do…). The second specificity of this model is its matte look. But I’ll come back to that point later.

The dial of this titanium YM42 is, however, classic. It retains most of the attributes of the white gold reference 226659, with oversized applied markers and hands in polished white gold and all tracks and printings in white. There is not a single touch of colour on this dial, which comes in a new colour named intense black, with a fine satin finish. Matte, with a velvet-like texture, this dial isn’t pure black like most of the watches in the brand’s collection but feels more like a very dark anthracite. Combined with a flat sapphire crystal with AR coating, the result is an almost complete lack of reflections. And the overall legibility, thanks to large markers and great contrast, is superb.

Inside the case is a classic Rolex movement, the calibre 3235 – used in the Submariner Date , the Datejust 41 and 36 , the Sea-Dweller or the Deepsea . A Superlative Chronometer (meaning certified by COSC and then by Rolex once the movement is encased), this automatic movement comes with all the recent innovations of Rolex: a bidirectional rotor on ball bearings, a Chronergy escapement, a paramagnetic nickel-phosphorus pallet fork and escape wheel and a paramagnetic blue Parachrom hairspring. It beats at 4Hz, stores a comfortable 70h power reserve and features an instantaneous date and a stop-seconds mechanism. Simply one of the best time-and-date engines on the market.

The bracelet of this new Yacht-Master 42 is also made of RLX Titanium. A classic 3-link Oyster style, it is also entirely matte with a so-called technical satin finish . Contrary to most Oyster bracelets, the sides are also matte, and only the Coronet on the clasp is polished. As you would expect, it is on par with Rolex quality standards, with an Oysterlock folding safety clasp and the Easylink comfort extension link to adjust the bracelet length by approximately 5mm. Also, this bracelet includes patented ceramic inserts inside the links to enhance its longevity and flexibility on the wrist.

Some thoughts… It is a disturbing watch (but a good one)

When you’ve been into watches for some years, you develop some preconceived notions about Rolex timepieces and the way they look and feel on the wrist. There’s a certain heft, a presence on the wrist, consolidating the perception of quality and solidity. There’s also a sheen that is unique to Rolex, with glossy bezels and dials. Rolex watches are so emblematic that your brain is pre-formatted to a certain conception of what they should look and feel like on the wrist. The Rolex Yacht-Master 42 RLX Titanium changes everything and breaks the norm. And it left me with mixed feelings, but not necessarily in a bad way.

When you take a Submariner and strap it around your wrist, it feels like home… It’s reassuringly heavy yet comfortable. Even though weight might be a bit irrational, weight adds to the feeling of quality and weight robustness. Having worn Rolex sports watches on so many occasions in my personal and professional life, I expect a watch from the Crown to weigh about 150/160 grams on a bracelet. It is a construction that is so deeply embedded in my brain that the moment I strapped the YM42 Titanium, I was left with a very disturbing sensation.

Yes, the watch looks like a Rolex, but it doesn’t feel like one on the wrist. It’s about 35% lighter than steel (around 100 grams), and everything I associated with how a Rolex should feel on the wrist simply vanished. I don’t want to sound too dramatic, but believe me when I say that it was a rather special experience at first. But the beauty is that you soon forget about this first impression and enjoy a watch that is surprisingly light. Despite its size, it is extremely comfortable and balanced. A watch that you’ll forget in about 30 seconds after you strapped it on the wrist. The initial feeling of a lack of robustness is, of course, just a misinterpretation of a pre-formatted brain and has nothing to do with the actual heft of the watch.

The second surprise with this watch is how it plays with the light and its lack of reflections. As said, most sports Rolex have a certain sheen. Even a Submariner or a Deepea feature glossy, reflective parts, such as the bezel and the sides of the case and the bracelet. The Yacht-Master 42 RLX Titanium is the most matte watch in the brand’s collection, with only a few polished accents (numerals on the bezel, bezel rim, and crown guards). Even the bevel on the side of the lugs is satin finished. This lack of sheen is definitely something new to Rolex and, far from me to complain, makes this model one of the most discreet and instrumental in the collection. It’s monochromatic, light on the wrist, and despite a size that I would have loved to be a bit smaller, a real joy to wear. It’s not a poser’s watch. The Yacht-Master 42 RLX Titanium is a tribute to when Rolex watches were made for a job. Yes, I’m very positive about this new release.

Availability & Price

The Rolex Yacht-Master 42 RLX Titanium 226627 isn’t yet available at retailers but will be soon. At least, on paper, as it won’t be easily accessible, even by Rolex standards. The brand doesn’t communicate production numbers, but we’ve heard that this will remain, for now, a rather exclusive model. It is priced at EUR 13,900 ,  CHF 13,400 or  USD 14,050 . More details at rolex.com .

Technical specifications – Rolex Yacht-Master 42 RLX Titanium 226627

10 responses.

RLX, which is Grade 5 Titanium. 🙂 I laughed out loud, because it’s just so typical for the obfuscation the brand foists upon us via their marketing behemoth on an annual basis.

A “Piece Unique” is more common than this model ….

I have been looking forward to this for a while. But, I was hoping/expecting a more adjustable bracelet. If they can do it on the Pelagos at 1/3 the price, there’s no excuse they can’t do it for this

I only know that the so-called special steel Rolex uses on the bezel of their regular models was soft and easily scratched on the Rolex I owned. My humble Tissot which I wore every day for years, was much more scratch resistant. I wasn’t impressed with the materials Rolex uses. They make them look impressive in photos.I thought Rolex was the watch to have, until I got one.Sold and moved on.

IWC Mark XX vs new Ingenuier – the internet melts down at how expensive the watch is using the same movement.

DJ36 vs YM42 RLX- the internet would do anything (deranged Sxual favors included) to pay double for the YM even though they have the same movement.

Quite like the look of this one on its own, but on the wrist it looks genuinly gigantic..

Old dog doing new trick? It’s tough

Well, it really looks nice, and if it is that big and only weights 100 grams, that is really awesome!!!! I love Titanium watches more than any gold watch. But I am of the opinion that although Rolex is a brand producing excellent watches with a QC second to none, it is still too expensive for my taste. I own since 2017 as part of my watch collection, an Ocean 7 Diver chronometer with an ETA 2824-2, saphire crystal, ceramic bezel, 2000m WR with He Valve, and it had never failed me as my daily watch, and I only paid for it 365.00 USD including taxes new. I also have a 1971 Submariner, and I use both daily,one on each wrist, and I don’t see any difference on the accuracy/function. But I have to pay a fortune every time the Rolex is serviced, not with the Ocean 7 that I can service myself- I am a pretty good amateur watchmaker myself, is one of my hobbies. And when I am working at the Hospital near the MRI machines with that strong magnetic field, I then use my Speedmaster Master Chronometer with cal.3861, and it works flawlessly, and it is less expensive than this YM. I like expensive watches, but still look for the best bang of my buck. And I am an owner of a JLC Deep Sea 40mm, a Blancpain Fifty Fatoms, a Zodiac Super Sea Wolf, a Juvenia 200M Diver’s, and an Omega SMPO as part of my diver’s watch collection. But I like this Titanium YM a lot more than the Submariner or the Sea Dweller. That is what I can say.

best thing about this watch is it’s an alternative to the biG piLLowized case of the redesigned submariner a few years back. softer scratchable titanium and no bezel lume (guess people only yacht in the daytime) are reasons not to buy.

Really, why does one need a 42mm titanium Rolex watch when sailing his uber racing yacht!?! One need to keep your eyes on the gyro compass, or the magnetic compass including (always) the trim of the sails including the large readout on the on the E-chart display which includes instantly time, speed, direction and position. Last: Why does one need a watch when sailing in a corrosive salt sea and air environment? Answer: Beats me unless one wishes to show off his wealth at the yacht club dinner during post racing presentations where a 42mm titanium Rolex is a must.

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Any other year, the titanium yacht-master 42 would steal the show for rolex. this year, the brand has so many crazy releases that the ym flies under the radar. here's why it still matters..

A year ago, the very idea of a titanium Rolex was relegated to wild dreams. A prototype had been seen on the wrist of British competitive sailor Sir Ben Ainslie, but the widely circulated online photo had gotten so old that some of us began to wonder if the watch would ever see the light of day.

The pic that launched our titanium dreams. Image by Ineos Britannia Team / C GREGORY

Now, in less than five months, we've gotten two watches from the Crown cased in RLX Titanium (a grade 5 titanium). The first was last year's 50mm Deep Sea Special , the mega dive watch that obliterated the water-resistance record. And now this week we have the Yacht-Master 42, which unlike the DSS is sized so that a normal human being could conceivably wear it. 

Here it is. The first practically sized titanium Rolex, the new Yacht-Master 42. 

It's a big deal. But when seen next to Daytonas with display casebacks, Day-Dates with emojis, a solid-gold GMT-Master II, and an entirely new line of dress watches, a titanium Yacht-Master barely moves the needle of surprise and excitement. What a wild 48 hours this has been for the House of Wilsdorf.

In some ways, it feels like the appropriate response to not be that excited. After all, at this point every other watchmaker under the sun has made a titanium watch, from affordable Citizens in multiple colors of bezels and dials to Jean-Claude Biver's $500,000 minute repeater tourbillon announced Sunday.

And yet, as soon as the new titanium Yacht-Master ref. 226627 started to be passed around the room of Hodinkee editors during this week's Watches & Wonders trade show, the general reaction was just to laugh with surprise. This 42mm watch, which looks so sturdy, feels so unbelievably light. I mean, that's titanium for you. But still. You can't quite believe this watch is real, on a number of different levels. 

For any of us who've ever tried on a steel Submariner (a.k.a. anyone with a passing interest in Rolex), it's kind of comical to find out how much your brain is preconditioned to see a 42mm steel Oyster case, round indices, and Mercedes hands and think about the luxurious heft that awaits you.

At around 100 grams, according to Rolex, the titanium Yacht-Master is so light it breaks your brain.

For a moment, let's compare the new YM to last year's titanium Pelagos from Rolex's sister brand Tudor. Rolex's choice to put the watch on a bracelet instead of a sportier Oysterflex makes the comparison obvious. I've now spent time with both pieces, and I prefer the Yacht-Master. 

The YM, like the Pelagos, is distinctly a tool watch – something that would have been hard to say about Yacht-Masters in the past. But the finishing a world apart, which is saying something for such an understated metal as titanium.

Rolex's proprietary grade 5 "RLX Titanium" (stronger than the grade 2 of the Pelagos) has the curious property of being equally able to be brushed satin or polished, which means it has the nice sharp and shiny chamfers that you'd like to see contrasted against the dark grey and relatively matte metal. That combination also works well with the more matte and textured dial – and with the contrast of the raised black numerals against a matte ceramic bezel insert, which is is the main giveaway that this is still squarely a Yacht-Master.

My main critique (which I share into the void, knowing that Rolex designers will do whatever they think best) is that I wish they'd  stuck to the no-date design of Ainsilie's prototype. In the practical application of most sailing races, there's really no use for a date. If you're blue-water sailing and circumnavigating the globe, maybe its useful, though just like dive watches the practical application gives way to the reality of technology. So why not refine the design further and leave the date off altogether? And while we're at it, a better quick-adjustment option would be great.

The price is somewhat immaterial – CHF 13,400 – since the average collector won't be able to get it at retail anytime soon. But the new Yacht-Master 42 is more than a solid release. It's a more than a titanium proof of concept. It's a wearable piece that portends at least the possibility of future experiments with this fascinating material. 

For more information visit Rolex. 

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.css-1c7en8u{font-size:clamp(1.375rem, 1.25rem + 0.3125vw, 3.125rem);line-height:1.1;margin-bottom:1rem;} Yacht-Master 42 .css-1g7r01k{font-weight:300;font-size:clamp(0.875rem, 0.9375rem + 0.1563vw, 1.25rem);line-height:1.2;text-wrap:balance;}.css-1g7r01k span{display:block;} Oyster, 42 mm, yellow gold Reference 226658

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Staying on course

The Oyster Perpetual Yacht-Master 42 in 18 ct yellow gold with a black dial and an Oysterflex bracelet.

The oysterflex bracelet, highly resistant and durable.

The Yacht-Master’s new Oysterflex bracelet, developed by Rolex and patented, offers a sporty alternative to metal bracelets. The bracelet attaches to the watch case and the Oysterlock safety clasp by a flexible titanium and nickel alloy metal blade.

The blade is overmoulded with high-performance black elastomer which is particularly resistant to environmental effects, very durable and perfectly inert for the wearer of the watch. For enhanced comfort, the inside of the Oysterflex bracelet is equipped with a patented longitudinal cushion system that stabilizes the watch on the wrist and fitted with an 18 ct yellow gold Oysterlock safety clasp. It also features the Rolex Glidelock extension system, designed by the brand and patented. This inventive toothed mechanism, integrated beneath the clasp, allows fine adjustment of the bracelet length by some 15 mm in increments of approximately 2.5 mm, without the use of tools.

18 ct yellow gold

Commitment to excellence

By operating its own exclusive foundry, Rolex has the unrivalled ability to cast the highest quality 18 ct gold alloys. According to the proportion of silver, copper, platinum or palladium added, different types of 18 ct gold are obtained: yellow, pink or white.

They are made with only the purest metals and meticulously inspected in an in-house laboratory with state-of-the-art equipment, before the gold is formed and shaped with the same painstaking attention to quality. Rolex's commitment to excellence begins at the source.

Bidirectional Rotatable Bezel

Timing the distance.

The Yacht-Master’s bidirectional rotatable 60-minute graduated bezel is made entirely from precious metals or fitted with a Cerachrom insert in high-tech ceramic. The raised polished numerals and graduations stand out clearly against a matt, sand-blasted background.

This functional bezel – which allows the wearer to calculate, for example, the sailing time between two buoys – is also a key component in the model’s distinctive visual identity.

Exceptional legibility

Like all Rolex Professional watches, the Yacht-Master 42 offers exceptional legibility in all circumstances, and especially in the dark, thanks to its Chromalight display.

The broad hands and hour markers in simple shapes – triangles, circles, rectangles – are filled with a luminescent material emitting a long-lasting glow.

More Yacht-Master technical details

Reference   226658

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Oyster, 42 mm, yellow gold

Oyster architecture

Monobloc middle case, screw-down case back and winding crown

Bidirectional rotatable 60-minute graduated bezel with matt black Cerachrom insert in ceramic, polished raised numerals and graduations

Winding crown

Screw-down, Triplock triple waterproofness system

Scratch-resistant sapphire, Cyclops lens over the date

Water resistance

Waterproof to 100 metres / 330 feet

Perpetual, mechanical, self-winding

3235, Manufacture Rolex

-2/+2 sec/day, after casing

Centre hour, minute and seconds hands. Instantaneous date with rapid setting. Stop-seconds for precise time setting

Paramagnetic blue Parachrom hairspring. High-performance Paraflex shock absorbers

Bidirectional self-winding via Perpetual rotor

Power reserve

Approximately 70 hours

Flexible metal blades overmoulded with high-performance elastomer

Folding Oysterlock safety clasp with Rolex Glidelock extension system

Highly legible Chromalight display with long-lasting blue luminescence

Certification

Superlative Chronometer (COSC + Rolex certification after casing)

Learn how to set the time and other functions of your Rolex watch by consulting our user guides.

Yacht-Master 42

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what is a research hypothesis and what are the different types

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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13 Different Types of Hypothesis

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.

A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.

Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .

Types of Hypothesis

Before you Proceed: Dependent vs Independent Variables

A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:

• The independent variable is the variable that is causing a change.
• The dependent variable is the variable the is affected by the change. This is the variable being tested.

Read my full article on dependent vs independent variables for more examples.

Example: Eating carrots (independent variable) improves eyesight (dependent variable).

1. Simple Hypothesis

A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.

This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.

You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.

Simple Hypothesis Examples

2. Complex Hypothesis

A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.

You can have multiple independent and dependant variables in this hypothesis.

Complex Hypothesis Example

In the above example, we have multiple independent and dependent variables:

• Independent variables: Age and weight.
• Dependent variables: diabetes and heart disease.

Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.

3. Null Hypothesis

A null hypothesis will predict that there will be no significant relationship between the two test variables.

For example, you can say that “The study will show that there is no correlation between marriage and happiness.”

A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.

A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”

Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.

Null Hypothesis Examples

4. Alternative Hypothesis

An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.

We use the symbol H A or H 1 to denote an alternative hypothesis.

The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.

The following statement is always true: H 0 ≠ H A .

Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”

We can have two hypotheses here:

• Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.”
• Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.”

For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.

5. Composite Hypothesis

A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.

Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.

But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”

We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.

Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.

6. Directional Hypothesis

A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.

Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.

We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.

Directional Hypothesis Examples

7. Non-Directional Hypothesis

A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.

These hypotheses predict an effect, but stop short of saying what that effect will be.

A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).

Let’s turn the above directional hypotheses into non-directional hypotheses.

Non-Directional Hypothesis Examples

8. Logical Hypothesis

A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.

These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.

Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.

Here are some examples:

• Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
• Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

9. Empirical Hypothesis

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.

We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.

So, an empirical hypothesis is a hypothesis that can and will be tested.

• Raising the wage of restaurant servers increases staff retention.
• Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
• Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.

Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).

A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.

It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.

This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.

You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.

Statistical Hypothesis Examples

• Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
• Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.

11. Associative Hypothesis

An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.

We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).

So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.

Associative Hypothesis Examples

• Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
• Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.

12. Causal Hypothesis

A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.

A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .

If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.

Causal Hypothesis Examples

13. Exact vs. Inexact Hypothesis

For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.

An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:

“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”

Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.

See Next: 15 Hypothesis Examples

This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .

[1] https://jnnp.bmj.com/content/91/6/571.abstract

[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Green Flags in a Relationship
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Signs you're Burnt Out, Not Lazy
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Toxic Things Parents Say to their Children
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Red Flags Early in a Relationship

2 thoughts on “13 Different Types of Hypothesis”

Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!

You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.

When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.

Cheers, Chris

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

• Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

It predicts the relationship between a single dependent variable and a single independent variable.

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

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I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

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• What Are The Types of Research Hypothesis? + [Examples]

It is vital to fully understand a hypothesis to address the types of research hypotheses. A hypothesis explains an established or known fact that has not yet been proven or validated.

Simply put, it is a statement explaining why and how a particular thing works based on philosophical assumptions and facts.

For example, a hypothesis goes like this;

A patient is likely to trust the pediatrician’s diagnosis based on the perception that the doctor is well-versed in the practice of medicine.

A hypothesis is a basis for scientific research or experiment, usually coined as a research hypothesis.

Three attributes or features measure the viability of a research hypothesis, and they are as follows.

• A research hypothesis must be specific, testable or measurable, and verifiable. In other words, the research hypothesis should create clear predictions than can be tested.
• Ideally, a hypothesis can be drawn from previous theoretical research publications.
• A good research hypothesis is much more than an intelligent guess, and sometimes, a research hypothesis could take the form of research questions that can be explored further via research and suggest an expected result.

Research hypotheses are a vital part of the scientific process that leads to or are the reasons for scientific experiments. That said, a slight flaw in constructing a hypothesis could generate negative results.

There are various types of hypotheses, and the following checklist should guide a good hypothesis.

• Is the language employed clear and direct?
• Is there a good relationship between the hypothesis and the research topic?
• Can the hypothesis be tested?
• What are the methods used to carry out testability?
• What are areas of explanation being addressed?

The essence of this checklist is to get your hypothesis up on the right footing and help you pinpoint any gaps or weaknesses.

The following listed below are the various 7 types of research hypotheses.

It can show the impact of a relationship between a single dependent variable and a single independent variable. For example,

Consuming too many fizzy drinks will cause weight gain and a bloated belly.

It foretells the relationship between multiple independent and dependent variables.

For instance, eating more vegetables and a low-calorie diet would lead to weight loss.

It shows the expected direction required to determine the relationship between variables and is derived from theory. Furthermore, it shows a researcher’s intellectual commitment to a particular outcome by the length of the study.

For example,

Toddlers under the age of 4 who were given well-balanced meals for 5 years showed a higher IQ level than their counterparts who did not have the same treatments.

It does not predict the direction or nature of the relationship between the two variables. A non-directional hypothesis is used mostly when there is no theory involved. For example, men and women differ in terms of helpfulness.

The associative hypothesis shows the interdependency between related variables. A change in one variable results will cause a change in the other variable. However, the change is not caused by either of the variables.

For example, the increase in the number of unhealthy people visiting a particular hospital is not because the hospital is the source of their illness. Rather it could be a result of other unrelated factors like the weather, personal hygiene practices, etc.

On the other hand, the causal hypothesis predicts the effect a change in variables would have on different variables. For instance, a change in the writing style on their blog led to higher user engagement.

This refers to a lack of relationship between different variables. For example, plants would grow irrespective of the source of water, natural or artificial. It proposes a negative statement to support the researcher’s discovery, showing that no relationship exists between the two variables.

The alternative hypothesis is a statement used in statistical experiments. It is the opposite of the null hypothesis and is described by the term H1 or Ha. The term alternative is used because it is the alternative to the null hypothesis. Therefore it is safe to say that it is an alternative theory to the one a researcher is testing and trying to prove.

The Alternative Hypothesis is classified into two categories;

Directional and Non-Directional.

• Directional: A statement outlining the ways the expected outcomes would be collated. It is mostly used in cases where there is a need to establish a relationship between two different things or when comparing various groups. For example, Attending physiotherapy sessions will improve the stage performance of ballerinas.
• Non-directional: This implies no direction for the expected results. For example, attending physiotherapy sessions impacts the stage performance of ballerinas.

The directional statement clearly states that the physiotherapy sessions would boost performance in both examples outlined above. At the same time, the non-directional only acknowledges that the sessions would influence performance without stating whether the influence would be positive or negative.

8. Empirical Hypothesis

When a theory is proven through an experiment and observation, this justifies or validates a claim and distinguishes it from a wild guess.

Here are a few examples that depict the empirical hypothesis:

a. Women who take folate supplements face a lesser risk of having children with congenital disabilities.

b. Good behavior in children can be reinforced when they are rewarded for good behavior.

9. Statistical Hypothesis

It is a statement that postulates a theory based on studying a sample population. It is a logic-based analysis where a specific population is researched to gather evidence to prove a particular theory.

For example:

43% of the American population in the age group of 22-29 speak a second language.

Testability in the hypothesis is crucial in establishing any scientific research in the physical world. This is because research or any science founded on a hypothesis is usually laced with inherent flaws. One of the flaws is the idea that any hypothesis by design significantly reduces the area of exploration, which births experimental results that would fail in real-life scenarios.

This problem is further compounded by modern science, which equates philosophical concepts to physical science. Testability solves these problems by making the research hypothesis more truthful, based on real tenable results. Hence any well-thought-out hypothesis would be founded in testability.

The condition for any viable hypothesis is testability. To be considered testable, the following criteria must be fulfilled.

• There must exist a viable means to prove that the hypothesis is true.
• Similarly, there must be a possibility to prove the hypothesis false.
• Finally, the result of the hypothesis must be replicable.

Without these testability criteria, the hypothesis and proposed results would be indefinite, and the significance of the experiment would be lost.

How To Formulate an Effective Research Hypothesis

There are clear and precise steps to creating an effective research hypothesis. An effective research hypothesis must answer these 6 questions;

What, who, where, when, how, and why?.

In the scientific method, the first step is to ask a question. Frame this question using the classic six highlighted above. For example:

• How long does it take avocados to grow?
• Why do we have shorter days and longer nights in winter?
• What happened to the groundnut pyramids?
• How does a caterpillar become a butterfly?
• Why are students excited on Friday afternoon?
• How does sleep affect motivation?
• Why do tax systems help build an economy?

So the first step is to identify and state what problem you are trying to solve. The hypothesis must clearly define the subject, the experiment’s focus, and the expected outcome.

Put together preliminary research data from a wide range of sources, including academic journals, personal experiments, and observations from the work of others. Afterward, define the variables, and separate the dependent variables from the independent variables.

The independent variables are the ones that are malleable and can be tweaked, controlled, changed, and affected by various conditions. Secondly, independent variables are isolated from other factors of the research.

On the other hand, dependent variables rely on other aspects of the research and are affected by any change in the independent variable.

Refine your hypothesis by emulating the following as a checklist:

• Specific language devoid of any ambiguity must be used.
• Clearly predict the relationship between the variables and the expected outcome.
• No prior assumptions should be made about the reader’s knowledge.
• The results must be testable, relevant, and specific to the research questions.

However, one of the proven methods of determining the effectiveness of your research hypothesis is to compare it to an already-existing hypothesis. It would help guide and make the process easier.

Here are a few general examples that can guide you in formulating your hypothesis:

a. Eating a generous amount of fiber-rich fruits like apples after age 50 would keep the doctor away or limit visits to the doctor’s office.

b. Cheap airlines, referred to as budget airlines, will receive more customer complaints than regular or premium airlines because of the limited amenities provided compared to full-service airlines.

Stating the obvious, the final step is to write your hypothesis using all the steps outlined. It is important to remember that your hypothesis is a statement that shows who or what is being studied, the variables, and your predicted outcome.

Hypothesis in Research

We have already established that a hypothesis is an idea or a statement based on tangible evidence that can be proven. A hypothesis in research is simply a statement concerning the predicted outcome of a scientific study. In this instance, it has to be specific, testable, and falsifiable.

Specific here refers to clarity about the parties involved and the expected results.

For example, a patient’s perception of a doctor’s experience breeds a higher level of trust in the doctor’s diagnosis.

This example depicts the clarity and directness of the subject. There is no ambiguity in the expectations of the relationship referred to.

Testability in research hypothesis is simply saying that the hypothesis must be provable. This means that the data gathered must be collected and observed in a thorough scientific process to assess the quality of the hypothesis. In other words, there must be a proven way to validate the claims of the hypothesis.

For example, the doctor referred to in the previous hypothesis can be validated by other patients’ perceptions of his competence and previous results from past diagnoses. A quantitative research approach using a large number of people would have been used to test the claims of this hypothesis.

The falsifiability in the research hypothesis means that the hypothesis can be refuted. This step is essential in validating or establishing the viability of the hypothesis. Hence there has to be an emphatic way of confirming if a hypothesis is false.

The claim is that life exists on planets like the earth. This claim cannot be a hypothesis because the only way to verify this would be to visit all planets in the world and come back with evidence of life. This claim is not disprovable.

So when conducting a hypothesis in research, it is vital to meet all these criteria to have an effective hypothesis.

Hypothesis in Statistics

A hypothesis in statistics is a legal claim about a subject within the framework of a statistical model. It is a process of statistical inference to determine if the data collated is inadequate to prove a hypothesis. The data used here can be gleaned from a large population. A statistical analyst verifies a hypothesis by analyzing a random sample of the population.

In this case, the random population sample is used to test 2 different hypotheses; the null hypothesis and the alternative hypothesis.

4 Steps of Statistical Hypothesis Testing

There is a four-step process used for statistical hypothesis testing.

• State only two hypotheses; that way, only one can be right.
• Create an analysis plan that shows how the data would be evaluated.
• Implement the plan by physically analyzing the sample data
• Analyze the result and either accept the hypothesis or state the plausible hypothesis based on the given data.

For example, if you want to carry a test on, say, 50% of exceptional college students come from wealthy homes.

The null hypothesis would be that 50%  of the students are from wealthy homes, while the alternative hypothesis would be that 50% of the students are not from wealthy homes.

A random sample of 100 students in the said college would be carried out via a survey, and the null hypothesis would be tested.

If 40 of those students are not from wealthy homes, then the 50% null hypothesis would be rejected, and the alternative hypothesis would be accepted.

Scientific Hypothesis

In the scientific hypothesis, the researcher’s expectation from the experiment is achieved following a scientific method outlined below:

• Create the question
• Carry out a background research
• Design an experiment
• Collect data
• Analyze the results
• Reach a conclusion
• Share the results

In the scientific hypothesis, the statement is a prediction; then, it evolves into a question, answered via research. It is at the point the hypothesis states the desired expectation. The next step after this is to test the hypothesis.

For example, the effect of Vitamin C supplements for a patient with cold symptoms is that the medication would help alleviate the effects of the cold.

As we established, a hypothesis predicts a relationship between variables that is yet to be proven. Creating a viable research hypothesis involves conducting research and broadening your knowledge about the subject via studying in other to choose the area of focus. Different types of hypotheses can be adopted to validate your predictions. The hypothesis should be testable in other to validate the claims.

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• research hypothesis
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How to Write a Research Hypothesis

• Research Process
• Peer Review

Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

An associative hypothesis says that when one variable changes, so does the other variable.

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

Make sure your research is error-free before you send it to your preferred journal . Check our our English Editing services to avoid your chances of desk rejection.

Jonny Rhein, BA

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7 Types of Research Hypothesis: Examples, Significance and Step-By-Step Guide

Introduction.

In any research study, a research hypothesis plays a crucial role in guiding the investigation and providing a clear direction for the research. It is an essential component of a thesis as it helps to frame the research question and determine the methodology to be used.

This article will provide a detailed analysis of research hypotheses in a thesis, highlighting their significance and qualities. It will also explore different types of research hypotheses and provide illustrative examples. Additionally, a step-by-step guide to developing research hypotheses and methods for testing and validating them will be discussed. By the end of this article, readers will have a comprehensive understanding of research hypotheses and their role in a thesis.

Understanding Research Hypotheses in a Thesis

A research hypothesis is a statement of expectation or prediction that will be tested by research. In a thesis, a research hypothesis is formulated to address the research question or problem statement . It serves as a tentative answer or explanation to the research question. The research hypothesis guides the direction of the study and helps in determining the research design and methodology.

The research hypothesis is typically based on existing theories, previous research findings, or observations. It is formulated after a thorough review of the literature and understanding of the research area. A well-defined research hypothesis provides a clear focus for the study and helps in generating testable predictions. By testing the research hypothesis, researchers aim to gather evidence to support or reject the hypothesis. This process contributes to the advancement of knowledge in the field and helps in drawing meaningful conclusions.

Significance of Research Hypotheses in a Thesis

One of the key significance of research hypotheses is that they help in organizing and structuring the research study. By formulating a hypothesis, the researcher defines the specific research question and identifies the variables that will be investigated. This helps in narrowing down the scope of the study and ensures that the research is focused and targeted.

Moreover, research hypotheses provide a framework for data collection and analysis. They guide the researcher in selecting appropriate research methods , tools, and techniques to gather relevant data. The hypotheses also help in determining the statistical tests and analysis techniques that will be used to analyze the collected data.

Another significance of research hypotheses is that they contribute to the advancement of knowledge in a particular field. By formulating hypotheses and conducting research to test them, researchers are able to generate new insights, theories, and explanations. This contributes to the existing body of knowledge and helps in expanding the understanding of a specific phenomenon or topic.

Furthermore, research hypotheses are important for establishing the validity and reliability of the research findings. By formulating clear and testable hypotheses, researchers can ensure that their study is based on sound scientific principles. The hypotheses provide a basis for evaluating the accuracy and generalizability of the research results.

In addition, research hypotheses are essential for making informed decisions and recommendations based on the research findings. They help in drawing conclusions and making predictions about the relationship between variables. This information can be used to inform policy decisions, develop interventions, or guide future research in the field.

Qualities of an Effective Research Hypothesis in a Thesis

An effective research hypothesis in a thesis possesses several key qualities that contribute to its strength and validity. These qualities are essential for ensuring that the hypothesis can be tested and validated through empirical research. The following are some of the qualities that make a research hypothesis effective:

1. Specificity: A good research hypothesis is specific and clearly defines the variables and the relationship between them. It provides a clear direction for the research and allows for precise testing of the hypothesis.

2. Testability: An effective hypothesis in research is testable, meaning that it can be empirically examined and either supported or refuted through data analysis. It should be possible to design experiments or collect data that can provide evidence for or against the hypothesis.

3. Clarity: A research hypothesis should be written in clear and concise language. It should avoid ambiguity and ensure that the intended meaning is easily understood by the readers. Clear language helps in communicating the hypothesis effectively and facilitates its evaluation.

4. Falsifiability: A strong research hypothesis is falsifiable, which means that it is possible to prove it wrong. It should be formulated in a way that allows for the possibility of obtaining evidence that contradicts the hypothesis. This is important for the scientific process as it encourages critical thinking and the exploration of alternative explanations.

5. Relevance: An effective research hypothesis is relevant to the research question and the overall objectives of the study. It should address a significant gap in knowledge or contribute to the existing body of literature. A relevant hypothesis adds value to the research and increases its significance.

6. Novelty: A good research hypothesis is original and innovative. It should propose a new idea or approach that has not been extensively explored before. Novelty in the hypothesis increases the potential for new discoveries and contributes to the advancement of knowledge in the field.

7. Coherence: An effective research hypothesis should be coherent and consistent with existing theories, concepts, and empirical evidence. It should align with the current understanding of the topic and build upon previous research. Coherence ensures that the hypothesis is grounded in a solid foundation and enhances its credibility.

8. Measurability: A research hypothesis should be measurable, meaning that it can be quantitatively or qualitatively assessed. It should be possible to collect data or evidence that can be used to evaluate the hypothesis. Measurability allows for objective testing and increases the reliability of the research findings.

By incorporating these qualities into the formulation of a research hypothesis, researchers can enhance the validity and reliability of their study.

Different Types of Research Hypotheses in a Thesis

In a thesis, there are several different types of research hypotheses that can be used to test the relationship between variables. These hypotheses provide a framework for the research and guide the direction of the study. Understanding the different types of research hypotheses is essential for conducting a comprehensive and effective thesis.

The null hypothesis is a statement that suggests there is no significant relationship between the variables being studied. It assumes that any observed differences or relationships are due to chance or random variation. The null hypothesis is denoted as H0 and is often used as a starting point for hypothesis testing.

The alternative hypothesis, also known as the research hypothesis, is a statement that suggests there is a significant relationship between the variables being studied. It contradicts the null hypothesis and proposes that the observed differences or relationships are not due to chance.

A directional hypothesis is a specific type of alternative hypothesis that predicts the direction of the relationship between variables. It states that there is a positive or negative relationship between the variables, indicating the direction of the effect.

Non-Directional Hypothesis

In contrast to a directional hypothesis, a non-directional hypothesis does not predict the direction of the relationship between variables. It simply states that there is a relationship between the variables without specifying the direction of the effect.

A statistical hypothesis is a hypothesis that is formulated based on statistical analysis. It involves using statistical tests to determine the likelihood of the observed data occurring under the null hypothesis.

Associative Hypothesis

An associative hypothesis suggests that there is a relationship between variables, but it does not imply causation. It indicates that changes in one variable are associated with changes in another variable.

Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between variables. It suggests that changes in one variable directly cause changes in another variable.

These different types of research hypotheses provide researchers with various options to explore and test the relationships between variables in a thesis. The choice of hypothesis depends on the research question, the nature of the variables, and the available data.

Illustrative Examples of Research Hypotheses in a Thesis

To better understand research hypotheses in a thesis, let’s explore some illustrative examples. These examples will demonstrate how hypotheses are formulated and tested in different research studies.

Example 1: Hypothesis for a study on the effects of exercise on weight loss:

Null Hypothesis (H0): There is no significant difference in weight loss between individuals who engage in regular exercise and those who do not.

Alternative Hypothesis (H1): Individuals who engage in regular exercise will experience greater weight loss compared to those who do not exercise.

Example 2: Hypothesis for a study on the impact of social media on self-esteem:

Null Hypothesis (H0): There is no significant relationship between social media usage and self-esteem levels.

Alternative Hypothesis (H1): Increased social media usage is associated with lower self-esteem levels.

Example 3: Hypothesis for a study on the effectiveness of a new teaching method in improving student performance:

Null Hypothesis (H0): There is no significant difference in student performance between the traditional teaching method and the new teaching method.

Alternative Hypothesis (H1): The new teaching method leads to improved student performance compared to the traditional teaching method.

These examples highlight the structure of research hypotheses, where the null hypothesis represents no effect or relationship, while the alternative hypothesis suggests the presence of an effect or relationship. It is important to note that these hypotheses are testable and can be analyzed using appropriate statistical methods.

Step-by-Step Guide to Developing Research Hypotheses in a Thesis

Developing a research hypothesis is a crucial step in the process of conducting a thesis. In this section, we will provide a step-by-step guide to developing research hypotheses in a thesis.

Step 1: Identify the Research Topic

The first step in developing a research hypothesis is to clearly identify the research topic. This involves understanding the research problem and determining the specific area of study.

Step 2: Conduct Preliminary Research

Once the research topic is identified, it is important to conduct preliminary research to gather relevant information. This helps in understanding the existing knowledge and identifying any gaps or areas that need further investigation.

Step 3: Formulate the Research Question

Based on the preliminary research, formulate a clear and concise research question. The research question should be specific and focused, addressing the research problem identified in step 1.

Step 4: Define the Variables

Identify the variables that will be studied in the research. Variables are the factors or concepts that are being measured or manipulated in the study. It is important to clearly define the variables to ensure the research hypothesis is specific and testable.

Step 5: Predict the Relationship and Outcome

The research hypothesis should propose a link between the variables and predict the expected outcome. It should clearly state the expected relationship between the variables and the anticipated result.

Step 6: Ensure Clarity and Conciseness

A good research hypothesis should be simple and concise, avoiding wordiness. It should be clear and free from ambiguity or assumptions about the readers’ knowledge. The hypothesis should also be observable and measurable.

Step 7: Validate the Hypothesis

Before finalizing the research hypothesis, it is important to validate it. This can be done through further research, literature review , or consultation with experts in the field. Validating the hypothesis ensures its relevance and novelty.

By following these step-by-step guidelines, researchers can develop effective research hypotheses for their theses. A well-developed hypothesis provides a solid foundation for the research and helps in generating meaningful results.

Methods for Testing and Validating Research Hypotheses in a Thesis

Hypothesis testing is a formal procedure for investigating our ideas about the world. It allows you to statistically test your predictions. The usual process is to make a hypothesis, create an experiment to test it, run the experiment, draw a conclusion, and then allow other researchers to replicate the study to validate the findings. There are several methods for testing and validating research hypotheses in a thesis.

Experimental Research

One common method is experimental research, where researchers manipulate variables and measure their effects on the dependent variable.

Observational Research

Another method is observational research, where researchers observe and record data without manipulating variables. This method is often used when it is not feasible or ethical to conduct experiments.

Survey Research

Survey research is another method that involves collecting data from a sample of individuals using questionnaires or interviews . This method is useful for studying attitudes, opinions, and behaviors.

Conducting Meta-analysis

In addition to these methods, researchers can also use existing data or conduct meta-analyses to test and validate research hypotheses. Existing data can be obtained from sources such as government databases, previous studies, or publicly available datasets. Meta-analysis involves combining the results of multiple studies to determine the overall effect size and to test the generalizability of findings across different populations and contexts. Once the data is collected, researchers can use statistical analysis techniques to analyze the data and test the research hypotheses. Common statistical tests include t-tests, analysis of variance (ANOVA), regression analysis, and chi-square tests.

The choice of statistical test depends on the research design, the type of data collected, and the specific research hypotheses being tested. It is important to note that testing and validating research hypotheses is an iterative process. Researchers may need to refine their hypotheses, modify their research design, or collect additional data based on the initial findings. By using rigorous methods for testing and validating research hypotheses, researchers can ensure the reliability and validity of their findings, contributing to the advancement of knowledge in their field.

In conclusion, research hypotheses are essential components of a thesis that guide the research process and contribute to the advancement of knowledge in a particular field. By formulating clear and testable hypotheses, researchers can make meaningful contributions to their field and address important research questions. It is important for researchers to carefully develop and validate their hypotheses to ensure the credibility and reliability of their findings.

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Frequently asked questions.

What is a hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Definition of a Hypothesis

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

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Hypothesis | Definition, Meaning and Examples

Hypothesis is a hypothesis is fundamental concept in the world of research and statistics. It is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables.

Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion . Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what hypothesis is, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

Table of Content

Characteristics of hypothesis, sources of hypothesis, types of hypothesis, functions of hypothesis, how hypothesis help in scientific research.

Hypothesis is a suggested idea or an educated guess or a proposed explanation made based on limited evidence, serving as a starting point for further study. They are meant to lead to more investigation.

It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Here are some common types of hypotheses:

Complex hypothesis, directional hypothesis.

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes. Example: Studying more can help you do better on tests. Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together. Example: How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live. A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing. Example: Drinking more sweet drinks is linked to a higher body weight score. Too much stress makes people less productive at work.

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes. Example: Drinking caffeine can affect how well you sleep. People often like different kinds of music based on their gender.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information. Example: The average test scores of Group A and Group B are not much different. There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one. Example: Patients on Diet A have much different cholesterol levels than those following Diet B. Exposure to a certain type of light can change how plants grow compared to normal sunlight.

Statistical Hypothesis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only. Example: The average smarts score of kids in a certain school area is 100. The usual time it takes to finish a job using Method A is the same as with Method B.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely. Example: Having more kids go to early learning classes helps them do better in school when they get older. Using specific ways of talking affects how much customers get involved in marketing activities.

Associative Hypothesis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing. Example: Regular exercise helps to lower the chances of heart disease. Going to school more can help people make more money.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change. Example: Playing violent video games makes teens more likely to act aggressively. Less clean air directly impacts breathing health in city populations.

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge . It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.

The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .

The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.

Hypothesis – FAQs

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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• Hypothesis is a logical prediction of certain occurrences without the support of empirical confirmation or evidence.
• In scientific terms, it is a tentative theory or testable statement about the relationship between two or more variables i.e. independent and dependent variable.

Different Types of Hypothesis:

1. Simple Hypothesis:

• A Simple hypothesis is also known as composite hypothesis.
• In simple hypothesis all parameters of the distribution are specified.
• It predicts relationship between two variables i.e. the dependent and the independent variable

2. Complex Hypothesis:

• A Complex hypothesis examines relationship between two or more independent variables and two or more dependent variables.

3. Working or Research Hypothesis:

• A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population.

4. Null Hypothesis:

• A null hypothesis is a general statement which states no relationship between two variables or two phenomena. It is usually denoted by H 0 .

5. Alternative Hypothesis:

• An alternative hypothesis is a statement which states some statistical significance between two phenomena. It is usually denoted by H 1 or H A .

6. Logical Hypothesis:

• A logical hypothesis is a planned explanation holding limited evidence.

7. Statistical Hypothesis:

• A statistical hypothesis, sometimes called confirmatory data analysis, is an assumption about a population parameter.

Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis . So, what is the difference between null hypothesis and alternate hypothesis? Let’s have a look:

Major Differences Between Null Hypothesis and Alternative Hypothesis:

Importance of Hypothesis:

• It ensures the entire research methodologies are scientific and valid.
• It helps to assume the probability of research failure and progress.
• It helps to provide link to the underlying theory and specific research question.
• It helps in data analysis and measure the validity and reliability of the research.
• It provides a basis or evidence to prove the validity of the research.
• It helps to describe research study in concrete terms rather than theoretical terms.

Characteristics of Good Hypothesis:

• Should be simple.
• Should be specific.
• Should be stated in advance.

References and For More Information:

https://ocw.jhsph.edu/courses/StatisticalReasoning1/PDFs/2009/BiostatisticsLecture4.pdf

https://keydifferences.com/difference-between-type-i-and-type-ii-errors.html

https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/a/consequences-errors-significance

https://stattrek.com/hypothesis-test/hypothesis-testing.aspx

http://davidmlane.com/hyperstat/A2917.html

https://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html

https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html

https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-why-we-need-to-use-hypothesis-tests-in-statistics

• Characteristics of Good Hypothesis
• complex hypothesis
• example of alternative hypothesis
• example of null hypothesis
• how is null hypothesis different to alternative hypothesis
• Importance of Hypothesis
• null hypothesis vs alternate hypothesis
• simple hypothesis
• Types of Hypotheses
• what is alternate hypothesis
• what is alternative hypothesis
• what is hypothesis?
• what is logical hypothesis
• what is null hypothesis
• what is research hypothesis
• what is statistical hypothesis
• why is hypothesis necessary

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

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Guide: What Are The Different Types and Styles of Stun Guns?

• August 16, 2024

Stun guns are a popular option for self-defense and they promise to keep you safe with a quick press of a button. But it can feel overwhelming when it comes time to pick out a stun gun. It's good to be careful and do your research before buying something you'll trust for your personal safety.

There are a lot of decisions out there for you to think about, too. From the typical handheld models to things like stun flashlights or stun knuckles, there's a lot of different options and varieties out there. 

If you're looking into self-defense options, the features and different types of stun guns can be pretty helpful to know before you buy. I'll compare stun guns to other self defense options, too.

Let's do this!

What are the basic types of stun guns, how much voltage and amperage is enough, are stun guns legal to own or carry, how to use a stun gun safely, less-lethal alternatives from byrna, less-lethal self defense.

Let's go over the different types available so you can choose what's best for your lifestyle.

First off, let's talk about the usual handheld stun guns , and they're compact and easy to carry. You can slip one into your pocket or handbag without any issues. Their small size doesn't lessen their effectiveness! But, some people might find their short range limiting - especially in some of the more stressful situations.

Have you ever thought about a stun baton ? With a longer reach, these can help you keep an attacker at a safer distance. Their bulkier nature might seem like a downside, but it also means they're less easy to hide and harder to carry, and they are perfect if you need a bigger weapon at home without giving up effectiveness. Safety first!

Now, let's talk about stun flashlights - they mix utility with self-defense and work as both a bright flashlight and a stun gun. I believe they are helpful for use in emergencies. Anyone might find this dual-job tool helpful but, if you want a purely defensive weapon, the extra functions can make it heavier and bigger.

Let's take a bit to look at some other less common types. Lipstick stun guns are discreet and designed to look just like a tube of lipstick, and they are perfect if you want maximum concealment. But remember, they usually have reduced voltage compared to bigger stun guns. You can only fit so big a battery into those small things.

There's also pen stun guns that blend with item while also giving a good self-defense tool. Some are made to look like rings and fit around your finger. But they're usually not as helpful as full-size models, either.

Next, there's cell phone stun guns . Designed to look like a regular smartphone, they have a hidden surprise. But be mindful of legal regulations around carrying these devices - because they might be restricted in some areas.

These get prettty creative - there are even cane or umbrella stun guns. These are perfect for people who naturally carry these items, like when walking or commuting, and they give you a longer reach and can blend with your attire. On the flip side, their size might draw unwanted attention.

For those interested in close-quarters tools, knuckle stun guns , similar to brass knuckles, might be your pick. Their design lets you deliver both a physical punch and an electric charge. But they might be less user-friendly if you're not comfortable with direct contact situations.

Let's talk about voltage and power next.

When you are picking a stun gun - you should think about the size, power, safety features, and price. So, where do you start with all these details?

One of the main things to think about is voltage and amperage. A stun gun with 50% more voltage doesn't necessarily have 50% more stopping power, but it does tell you a bit about its ability to work through thicker clothing. Let's break these down.

Stun guns come in a lot of kinds of voltage levels. Low Voltage models usually range from 50,000 to 100,000 volts and are good for causing pain and muscle contractions. You might find these helpful if you need a basic deterrent. They might have somewhere between 1-2 mA ( milliamps ). 

Medium Voltage units, ranging from 100,000 to 300,000 volts, are better at going through clothing and causing disorientation. That's helpful when heavier clothing is a problem. High-voltage models span from 300,000 volts to more, and they give a powerful shock through thick clothing. These cause extreme pain and muscle spasms. If you expect confrontations that need the utmost stopping power, these are a good choice! They're probably around 2-3 mA, sometimes as high as 4. 

You also need to think about the charge delivered. That's measured in microcoulombs (µC) , usually between 0.5-4.5. This is basically a metric that considers both amps and time - how much electricity flows and how long it flows. This is a popular industry metric that is a common industry comparison of how strong stun guns are.

Now, let's talk about size . A lot of people like small models that are easy to carry and hide. You should know that smaller stun guns might have less features or less power compared to bigger ones - think about what you need more: portability or extra power and features.

Safety features are another thing to think about. Look for stun guns that come with disable pins, built-in flashlights, and safety switches. These features add layers of security by stopping accidental discharges. So, the device is ready when you need it. We'll talk about safety more in a bit.

Also, let's not forget about the price . High-end stun guns sometimes come with advanced features and higher voltage, but they can be expensive. Budget models may not have a lot of advanced features, but they can still be helpful deterrents!

Choose your budget and then weigh the pros and cons. Finally, when you make the right choice, you have to balance these factors according to what you need. Safety first!

With the legality of owning or carrying a stun gun, you know the rules can be quite complicated. It sometimes depends on where you live, too. I've seen that, for the most part, stun guns are legal in a lot of states across the U.S., but specific rules can create some issues. Did you know that Hawaii and Rhode Island - just to give you an example! - completely prohibit the ownership of stun guns? Are there age needs, you might ask? Usually, yes. Most states demand that you be 18 years or older to make a buy.

Permits are another area where laws can change quite a bit. In a lot of states, you don't need a permit to own or carry a stun gun. But local rules can sometimes add extra obstacles. Just think about it, a few cities might have specific bans or regulations that you'll need to follow. It's a good idea to check your local laws to stay away from accidentally breaking any rules too. 

Concealed carry is yet another layer of legal factors you should know about. Some states need you to have a concealed carry permit if you plan to carry a stun gun in a hidden way.

Also, background checks can be used to enter the picture. Although most states don't need these checks to buy a stun gun, some do. That means you might have to go through one, depending on where you live. These differences can catch you off guard.

And what about carrying stun guns when traveling? When flying, remember that the TSA doesn't allow stun guns in carry-on luggage. But you can pack them in checked baggage if they are unloaded and the right way secured. Always a good tip to keep in mind.

Need some advice on staying updated with legal changes? It's always smart to frequently check local and state laws, because they can change. You should subscribe to legal updates or newsletters that attention to self-defense weapon regulations too. Websites of local law enforcement agencies also sometimes give you the latest information on this topic. Ignoring these updates can get you in legal trouble after years of compliant ownership - don't skip this.

Remember, staying proactive and in tune with legalities helps you stay within the law, to make sure that you can carry a stun gun without unexpected legal issues.

When you're ready to use a stun gun, you have to think about safety for both you and other people around you! But, how do you make sure you're it the right way? I give you a guide on safe usage that breaks it down step-by-step. That guide covers different types and styles.

First, try to aim for large muscle groups like the shoulders, back, and chest. Especially the area between the neck and waist - these areas are hard for an attacker to push away. That increases your chances of stopping them. How long should you keep it in contact? Use the stun gun for at least 3 to 5 seconds to stop the attacker - if the threat continues, keep the contact for more time if needed.

Stun guns can work through clothing , and how well they work can vary on the strength of the device. We talked about how devices with higher voltage/amps can penetrate clothing better, so think about the strength you need. And what about handling and storage? Practice drawing and handling your stun gun sometimes to make sure you can get to it quickly if there's an emergency. 

You should also think about legal factors. Make sure you know the local laws about stun guns to stay away from legal trouble. Use the device only in cases of immediate danger and never for intimidation or misuse.

Let's talk about battery maintenance, too - keep your stun gun's batteries completely charged and working well before you carry it. Who wants their stun gun to fail because of a dead battery? Always be aware of your surroundings so you can note potential threats early. A stun gun comes with great responsibility, so stay vigilant and ready.

Let's talk about the less-lethal options we give you at Byrna. Have you ever worried about the legal or ethical side of owning a gun for self-defense? Well, these 50 state legal CO2-powered options might be a good non-lethal option for you.

At Byrna , we have a number of CO2-powered products that shoot kinetic and chemical irritant projectiles , and these tools help you in different situations. Now, let's check out one of my favorite handheld options! The Byrna SD , a compact pistol, shoots 68-caliber rounds up to 60 feet, and they are easy to use and reliable. That makes it a good choice for anyone needing an easy self-defense tool. Priced around $379.99, it's an easy entry point into the less-lethal defense world.

People who need more power and performance should look at the Byrna LE . You get a better experience because this version has higher energy output and muzzle velocity. That gives you greater stopping power. At $479.99, it costs more than the SD model, but it might be worth it if you need something higher-powered.

Need even more range and power?

Byrna also has rifles like the Mission 4 and TCR . These shoulder-fired weapons deliver more power and longer range. Mission 4 and TCR are good for times when you need to handle threats from a distance, and they are both reliable and accurate. We price these between $599.99 and $699.99, depending on the model and what kind of features you pick.

What makes our products stand out is their design - directed at stopping attackers without causing deadly harm - that makes them a great option for people who want strong self-defense decisions without the serious downsides of firearms. CO2-powered projectiles make sure you have the stopping power you need, with less danger of permanent harm.

So, why wait? Check out our products at Byrna and build your self-defense plan!

When you're looking into the voltage levels of stun guns - handling the legal things - or checking into safe usage and maintenance, every little detail matters in building your confidence and feeling ready. Isn't it kind of interesting how something like a compact stun gun can make a difference in how we feel about our safety?

Now, try to think past stun guns for a bit. What do you think is the most factor when you're picking a self-defense tool?

Is it how easy it is to use, or maybe the surprise ingredient of hidden designs? Everyone has their own priorities and habits that make picking the right tool a very personal choice. Let's face it! Peace of mind is something we all value. Reliable and less-lethal options for self-defense can help with that. There's a reason police have different tools at their disposal - when words fail, and lethal force is not a good option either, less-lethal force can be a great way to get out of harm's way and dispel a threat. That's where pepper guns and non-lethal items come in helpful.

Byrna Technologies is leading the way in giving helpful and innovative self-defense tools that are also easy to get. Our pull-pierce CO2 cartridge system makes sure that Byrna products are ready when you need them most. It's comforting to know that these tools are legal in all 50 states , and they don't need any background checks and give you peace of mind without the trouble of traditional firearms.

Our products include pistols , rifles , different projectiles , pepper sprays , personal sirens , and even Ballistipac backpacks . There's something for everyone's needs and preferences. It's also great to know that Byrna products can be packed in your checked luggage when you travel! Just leave out the CO2 cartridges.

Why not visit Byrna.com to check out our catalog? With more than 20,000 five-star reviews from happy users around the country, you're sure to find something that meets your needs and improves your personal security.

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Money blog: Morrisons admits it 'went too far' with self-checkouts - as it changes strategy

Welcome to the Money blog, your place for personal finance and consumer news and tips. Today's posts include Morrisons getting rid of some self-checkouts and a Money Problem on topping up your national insurance. Leave your consumer issue below - remember to include contact details.

Monday 19 August 2024 20:13, UK

• Energy bills to rise 9% this winter - forecast
• Morrisons admits it went too far with self-checkouts
• Kellogg's shrinks size of Corn Flakes

Essential reads

• Money Problem : 'Should I top up my national insurance and could it really get me £6,000 extra?'
• Pay at every supermarket revealed - and perks staff get at each
• Couples on how they split finances when one earns more than other

Tips and advice

• All discounts you get as student or young person
• Save up to half price on top attractions with this trick
• Fines for parents taking kids out of school increasing

Ask a question or make a comment

Morrisons has admitted it "went a bit too far" with self-checkouts.

Chief executive Rami Baitiéh says the supermarket is "reviewing the balance between self-checkouts and manned tills".

Some will be removed.

Mr Baitiéh told The Telegraph : "Morrisons went a bit too far with the self-checkout. This had the advantage of driving some productivity. However, some shoppers dislike it, mainly when they have a full trolley."

The executive also said self checkouts had driven more shoplifting.

What have other supermarkets said about self-checkouts?

In April, the boss of Sainsbury's said customers liked self-checkouts...

That prompted us to ask readers for their thoughts - and we carried out a poll on LinkedIn which suggested the Sainsbury's boss was right...

Asda's chief financial officer Michael Gleeson said last week the technology had reached its limit - and said his firm would be putting more staff on tills.

Northern grocer Booths ditched almost all self-checkouts last year amid customer service concerns.

Over at Marks & Spencer, chairman Archie Norman last year blamed self-checkouts for a rise in "middle-class shoplifting".

But Tesco CEO Ken Murphy is an advocate: "We genuinely believe, at the end of the day, it provides a better customer experience."

The number of drivers visited by bailiffs due to unpaid traffic fines has increased substantially, according to a report.

Four million penalty charge notices (PCNs) were referred to bailiffs in England and Wales in the 2023-24 financial year, it is claimed.

This is up from 2.4 million during the previous 12 months, 1.9 million in 2019-20 and 1.3 million in 2017-18.

Read more ...

Ted Baker is the latest in a string of high-street giants to call in administrators in recent years, with shops set to disappear this week.

But how does it affect you? 

Purchases and returns

You can still buy items online and in store until they close, but you could run into trouble returning them. 

If the retailer stops trading, it may not be able to get your money back to you.

If that is the case, you would have to file a claim with Teneo (Ted Baker's administrator) to join a list of creditors owed money by Ted Baker – and even then there's no guarantee you'd get your money back.

If you have a gift card, you need to use it while you still can.

Credits and debits

You can file a claim with your debit or credit card provider to recover lost funds - but how exactly does that work?

• Credit card:  If you bought any single item costing between £100-£30,000 and paid on a credit card, the card firm is liable if something goes wrong. If any purchase was less than £100, you may still be able to get your money back via chargeback;
• Debit card:  Under chargeback, your bank can try to get your money back from Ted Baker's bank. However, be aware that this is not a legal requirement and it can later be disputed and recalled.

Many retailers boosted wages after living wage/minimum wage changes in spring.

Figures show German discount brands Aldi and Lidl top the list of major UK supermarkets when it comes to staff hourly pay - after Lidl introduced its third pay increase of the year in May to match its closest rival.

Meanwhile, Morrisons is at the bottom of the pack for staff pay outside London, with hourly wages starting at the National Living Wage (£11.44).

How do other companies compare when it comes to pay and benefits? We've taken a look...

Pay: £12.40 an hour outside London and £13.65 inside the M25

Aldi announced in March it was bringing in its second pay rise of the year as part of its aim to be the best-paying UK supermarket.

From 1 June, hourly pay rose from £12 an hour to £12.40 outside the M25 and £13.55 to £13.65 in London. 

Aldi is one of the only supermarkets to give staff paid breaks. It also offers perks such as discounted gym membership and cinema tickets, and financial planning tools. However, there are no cheaper meals, staff discounts or bonus schemes.

Pay:  £12.04 an hour outside London and £13.21 inside the M25

As of 1 July, hourly wages for Asda supermarket staff rose to £12.04 per hour from £11.11, with rates for London staff also going up to £13.21.

As part of the July changes, Asda brought in the option for free later-life care or mortgage advice. The company also offers a pension and a free remote GP service.

Pay:  £12 an hour outside London and £13.15 inside the M25

Co-op boosted its minimum hourly wage for customer team members from £10.90 to £12 nationally as the national living wage rose to £11.44 in April.

For staff inside the M25, rates rose from £12.25 to £13.15.

The perks are better than some. Workers can get 30% off Co-op branded products in its food stores as well as 10% off other brands. Other benefits include a cycle to work scheme, childcare vouchers and discounts on its other services.

Pay:  £11.50 an hour outside London and £12.65 inside the M25

Iceland says it pays £11.50 for staff aged 21 and over - 6p above the minimum wage. Employees in London receive £12.65 per hour.

Staff are also offered a 15% in-store discount, which was raised from 10% in 2022 to help with the cost of living.

The firm says it offers other perks such as a healthcare scheme and Christmas vouchers.

Pay:  £12.40 an hour outside London and £13.65 inside the M25

From June, Lidl matched its rival Aldi by raising its hourly wage to £12.40 for workers outside the M25 and £13.55 for those inside.

Lidl also offers its staff a 10% discount card from the first working day, as well as other perks such as dental insurance and fertility leave. 

Marks and Spencer's hourly rate for store assistants was hiked from £10.90 to £12 for staff outside London and from £12.05 to £13.15 for London workers from April.

The grocer also offers a 20% staff discount after the probation period as well as discretionary bonus schemes and a free virtual GP service.

Pay:  £11.44 an hour outside London and £12.29 inside the M25

Along with many other retailers, Morrisons increased the hourly wage for staff outside the M25 in line with the national living wage of £11.44 in April.

Employees in London receive an 85p supplement.

While it's not the most competitive for hourly pay, Morrisons offers perks including staff discounted meals, a 15% in-store discount and life assurance scheme.

Sainsbury’s

Sainsbury's hourly rate for workers outside London rose to £12 from March, and £13.15 for staff inside the M25.

The company also offers a 10% discount card for staff to use at Sainsbury's, Argos and Habitat, as well as a range of benefits including season ticket loans and long service rewards.

Pay:  £12.02 an hour outside London and £13.15 inside the M25

Since April, Tesco staff have been paid £12.02 an hour nationally - up from £11.02 - while London workers get £13.15 an hour.

The supermarket giant also provides a 10% in-store discount, discounted glasses, health checks and insurance, and free 24/7 access to a virtual GP.

Staff get their pay boosted by 10% on a Sunday if they joined the company before 24 July 2022.

Pay:  £11.55 an hour outside London and £12.89 inside the M25

Waitrose store staff receive £11.55 an hour nationally, while workers inside the M25 get at least £12.89.

Staff can also get access to up to 25% off at Waitrose's partner retailer John Lewis as well as 20% in Waitrose shops. 

JLP (the John Lewis Partnership) gives staff a bonus as an annual share-out of profit determined by the firm's performance. In 2021-22 the bonus was 3% of pay; however, it has not paid the bonus for the past two years.

Dozens of Ted Baker stores will shut for the last time this week amid growing doubts over a future licensing partnership with the retail tycoon Mike Ashley.

Sky News understands that talks between Mr Ashley's Frasers Group and Authentic, Ted Baker's owner, have stalled three months after it appeared that an agreement was imminent.

Administrators are overseeing the closure of its remaining 31 UK shops.

One store source said they had been told that this Tuesday would be the final day of trading.

The housing market experienced a surge in activity following the Bank of England's recent decision to cut interest rates, according to a leading property website.

Estate agents reported a 19% jump in enquiries about properties for sale after 1 August, when compared with the same period last year, research by Rightmove found.

It came after the Bank cut rates for the first time in more than four years from 5.25% to 5%.

The lead negotiator for major train union ASLEF has denied the union sees the new government as a "soft touch" after announcing fresh strikes two days after train drivers were offered a pay deal.

Drivers working for London North Eastern Railway will walk out on weekends from the end of August in a dispute over working agreements.

Lead negotiator Nigel Roebuck said it is a separate issue from the long-running row over pay, which looks likely to be resolved after a much-improved new offer from the government.

Over 40 bottles of fake vodka have been seized from a shop in Scotland after a customer reported "smelling nail varnish".

The 35cl bottles, fraudulently labelled as the popular brand Glen's, were recovered from the shop in Coatbridge, North Lanarkshire.

Officers from the council's environmental health officers and Food Standard Scotland (FSS) sent them for analysis after a customer raised the alarm by saying they smelt nail varnish from one of the bottles.

The bottles were found to be counterfeit.

Britons don't have long left to claim cost of living assistance from the Household Support Fund.

Introduced in October 2021, the scheme provides local councils with funding which can be used to support those struggling most with the rising cost of living.

The vast majority of councils operate their version of the Household Support Fund on a "first come, first serve" basis and will officially end the schemes once the funding has run out in September.

The help provided by councils has ranged from free cash payments, council tax discounts, and vouchers for supermarkets and energy providers.

Who is eligible?

Local authorities were instructed to target the funding at "vulnerable households in most need of support to help with significantly rising living costs" when it was first rolled out.

In particular, councils were guided to make priority considerations for those who: 

• Are eligible but not claiming qualifying benefits;
• Became eligible for benefits after the relevant qualifying dates;
• Are receiving housing benefit only;
• Are normally eligible for benefits but who had a nil award in the qualifying period.

If you do not meet these criteria, you can still contact your local council , with many having broadened their criteria for eligibility.

By Daniel Binns, business reporter

Weapons maker BAE Systems is the big loser on the FTSE 100 this morning, with its shares down almost 3% in early trading.

It comes following reports over the weekend that the German government is planning to scale back aid to Ukraine in its war with Russia – in what would be a blow to the arms industry.

German media said ministers are set to slash support for Kyiv to 6% of current levels by 2027 in their upcoming budget.

However, the government there has rejected the reports and has denied it is "stopping support" to Ukraine.

Whatever the truth, the reports appear to have spooked traders.

Other companies involved in the defence sector, including Rolls-Royce Plc and Chemring Group, are also down more than 2% and 1% respectively on Monday.

It comes amid a slight slump in early trading, with the FTSE 100 down just over 0.2%, although the FTSE 250 is up 0.07%.

Gainers this morning include housebuilders Barratt Developments, up 1.5%, and Redrow Plc, which is up almost 3%.

Barratt said today it intends to push ahead with a planned £2.5bn merger with its rival despite concerns from the competition regulator.

Meanwhile, the price of oil is down amid concerns of weaker demand in China.

Ongoing ceasefire talks in the Israel-Hamas conflict have also raised hopes of cooling tensions in the Middle East, which would help ease supply risks and worries.

A barrel of the benchmark Brent Crude is currently priced at just over $79 (£61).

On the currency markets, this morning £1 buys $1.29 US or €1.17.

Winter energy bills are projected to rise by 9%, according to a closely watched forecast.

The price cap from October to December will go up to £1,714 a year for the average user, Cornwall Insight says.

It would be a £146 rise from the current cap, which is controlled by energy regulator Ofgem and aims to prevent households on variable tariffs being ripped off.

The cap doesn't represent a maximum bill. Instead it creates an average bill by limiting how much you pay per unit of gas and electricity, as well as setting a maximum daily standing charge (which all households must pay to stay connected to the grid).

Ofgem will announce the October cap this Friday.

"This is not the news households want to hear when moving into the colder months," said the principal consultant at Cornwall, Dr Craig Lowrey.

"Following two consecutive falls in the cap, I'm sure many hoped we were on a steady path back to pre-crisis prices. 

"However, the lingering impact of the energy crisis has left us with a market that's still highly volatile and quick to react to any bad news on the supply front.

"Despite this, while we don't expect a return to the extreme prices of recent years, it's unlikely that bills will return to what was once considered normal. Without significant intervention, this may well be the new normal."

Cornwall Insight warned that the highly volatile energy market and unexpected global events, such as the recent escalating tensions in the Russia-Ukraine war, could see prices rise further at the start of the new year.

To avoid this vulnerability, Cornwall Insight said domestic renewable energy production should increase and Britain should wean itself off energy imports.

Kellogg's appears to have shrunk its packets of Corn Flakes. 

Two of its four different pack sizes have reduced in weight by 50g, according to The Sun. 

What used to be 720g boxes are now 670g, while 500g boxes have become 450g. 

The newspaper says the 670g boxes are being sold for £3.20 in Tesco - the same price customers were paying for the larger box back in May. 

The 450g boxes are being sold for £2.19, only slightly less than the previous price of £2.25.

Other supermarkets have similar pricing, although in Morrisons the price has gone down in proportion to the size reduction.

The 250g and 1kg pack sizes remain unchanged. 

Kellogg's has said it is up to shops to choose what they charge, but Tesco said the manufacturer should comment on pricing. 

Sky News has contacted Kellogg's for comment.

A spokesperson is quoted by The Sun: "Kellogg's Corn Flakes are available in four different box sizes to suit different shopper preferences and needs. 

"As the cost of ingredients and production processes increase, it costs us more to make our products than it used to.

"This can impact the recommended retail price. It's the grocer's absolute discretion and decision what price to charge shoppers."

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A research hypothesis is an assumption or a tentative explanation for a specific process observed during research. Unlike a guess, research hypothesis is a calculated, educated guess proven or disproven through research methods. ... Different Types of Hypotheses‌ ... Now that you have a firmer grasp on what a good hypothesis constitutes, the ...

It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.

The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones. In this blog, we'll learn what a research hypothesis is, why it's important in research, and the different types used in science.

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a 'working hypothesis'. We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments.

Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...

A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.

6. Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0, while the alternative hypothesis is H 1 or H a.

There are seven different types of research hypotheses. Simple Hypothesis. A simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. Complex Hypothesis. A complex hypothesis predicts the relationship between two or more independent and dependent variables. Directional Hypothesis.

An overview of the research hypothesis. As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. ... Different types of hypotheses. Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve ...

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher's findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. ...

This refers to a lack of relationship between different variables. For example, plants would grow irrespective of the source of water, natural or artificial. It proposes a negative statement to support the researcher's discovery, showing that no relationship exists between the two variables. 7. Alternative Hypothesis.

Research hypothesis checklist. Once you've written a possible hypothesis, make sure it checks the following boxes: It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis. It must include a dependent and independent variable: At least one independent variable ( cause) and one dependent ...

To form a solid theory, the vital first step is creating a hypothesis. See the various types of hypotheses and how they can lead you on the path to discovery. To form a solid theory, the vital first step is creating a hypothesis. ... Different Types in Science and Research By Jennifer Betts, B.A. , Staff Writer . Updated September 24, 2021 ...

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess — it should be based on ...

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...

Research Hypothesis Research is meant to be a valuable exploration that builds on existing knowledge. As such, a hypothesis is meant to be a brave prediction of what may happen in an experiment. This can be the null hypothesis or it can be a predication of a relationship between the independent and dependent variables.

Here are a few different types of hypotheses: Simple hypothesis: A simple hypothesis predicts a relationship between an independent and a dependent variable. Complex hypothesis: A complex hypothesis looks at the relationship between two or more independent variables and two or more dependent variables. Empirical hypothesis: An empirical ...

Research Hypothesis. Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely. ... leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in ...

A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population. ... Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis. So, what is the difference between null hypothesis ...

Otherwise, there is nothing to test! You have to state the null hypothesis (which is what we test) and the alternative hypothesis (which is what we expect). ... explaining in normal English what each one means in terms of the research question. ... This step is where the vast majority of differences in future chapters will arise: different ...

There are a lot of decisions out there for you to think about, too. From the typical handheld models to things like stun flashlights or stun knuckles, there's a lot of different options and varieties out there. If you're looking into self-defense options, the features and different types of stun guns can be pretty helpful to know before you buy.

It would cost £907.40 to cover all NI contributions from the 2023-24 tax year - each year is different but this is a good guide. Going back to your question, if you went on to enjoy 20 years of ...