user research hypothesis example

UX Research: Objectives, Assumptions, and Hypothesis

by Rick Dzekman

An often neglected step in UX research

Introduction

UX research should always be done for a clear purpose – otherwise you’re wasting the both your time and the time of your participants. But many people who do UX research fail to properly articulate the purpose in their research objectives. A major issue is that the research objectives include assumptions that have not been properly defined.

When planning UX research you have some goal in mind:

• For generative research it’s usually to find out something about users or customers that you previously did not know
• For evaluative research it’s usually to identify any potential issues in a solution

As part of this goal you write down research objectives that help you achieve that goal. But for many researchers (especially more junior ones) they are missing some key steps:

• How will those research objectives help to reach that goal?
• What assumptions have you made that are necessary for those objectives to reach that goal?
• How does your research (questions, tasks, observations, etc.) help meet those objectives?
• What kind of responses or observations do you need from your participants to meet those objectives?

One approach people use is to write their objectives in the form of research hypothesis. There are a lot of problems when trying to validate a hypothesis with qualitative research and sometimes even with quantitative.

This article focuses largely on qualitative research: interviews, user tests, diary studies, ethnographic research, etc. With qualitative research in mind let’s start by taking a look at a few examples of UX research hypothesis and how they may be problematic.

Research hypothesis

Example hypothesis: users want to be able to filter products by colour.

At first it may seem that there are a number of ways to test this hypothesis with qualitative research. For example we might:

• Observe users shopping on sites with and without colour filters and see whether or not they use them
• Ask users who are interested in our products about how narrow down their choices
• Run a diary study where participants document the ways they narrowed down their searches on various stores
• Make a prototype with colour filters and see if participants use them unprompted

These approaches are all effective but they do not and cannot prove or disprove our hypothesis. It’s not that the research methods are ineffective it’s that the hypothesis itself is poorly expressed.

The first problem is that there are hidden assumptions made by this hypothesis. Presumably we would be doing this research to decide between a choice of possible filters we could implement. But there’s no obvious link between users wanting to filter by colour and a benefit from us implementing a colour filter. Users may say they want it but how will that actually benefit their experience?

The second problem with this hypothesis is that we’re asking a question about “users” in general. How many users would have to want colour filters before we could say that this hypothesis is true?

Example Hypothesis: Adding a colour filter would make it easier for users to find the right products

This is an obvious improvement to the first example but it still has problems. We could of course identify further assumptions but that will be true of pretty much any hypothesis. The problem again comes from speaking about users in general.

Perhaps if we add the ability to filter by colour it might make the possible filters crowded and make it more difficult for users who don’t need colour to find the filter that they do need. Perhaps there is a sample bias in our research participants that does not apply broadly to our user base.

It is difficult (though not impossible) to design research that could prove or disprove this hypothesis. Any such research would have to be quantitative in nature. And we would have to spend time mapping out what it means for something to be “easier” or what “the right products” are.

Example Hypothesis: Travelers book flights before they book their hotels

The problem with this hypothesis should now be obvious: what would it actually mean for this hypothesis to be proved or disproved? What portion of travelers would need to book their flights first for us to consider this true?

Example Hypothesis: Most users who come to our app know where and when they want to fly

This hypothesis is better because it talks about “most users” rather than users in general. “Most” would need to be better defined but at least this hypothesis is possible to prove or disprove.

We could address this hypothesis with quantitative research. If we found out that it was true we could focus our design around the primary use case or do further research about how to attract users at different stages of their journey.

However there is no clear way to prove or disprove this hypothesis with qualitative research. If the app has a million users and 15/20 research participants tell you that this is true would your findings generalise to the entire user base? The margin of error on that finding is 20-25%, meaning that the true results could be closer to 50% or even 100% depending on how unlucky you are with your sample.

Example Hypothesis: Customers want their bank to help them build better savings habits

There are many things wrong with this hypothesis but we will focus on the hidden assumptions and the links to design decisions. Two big assumptions are that (1) it’s possible to find out what research participants want and (2) people’s wants should dictate what features or services to provide.

Research objectives

One of the biggest problem with using hypotheses is that they set the wrong expectations about what your research results are telling you. In Thinking, Fast and Slow, Daniel Kahneman points out that:

• “extreme outcomes (both high and low) are more likely to be found in small than in large samples”
• “the prominence of causal intuitions is a recurrent theme in this book because people are prone to apply causal thinking inappropriately, to situations that require statistical reasoning”
• “when people believe a conclusion is true, they are also very likely to believe arguments that appear to support it, even when these arguments are unsound”

Using a research hypothesis primes us to think that we have found some fundamental truth about user behaviour from our qualitative research. This leads to overconfidence about what the research is saying and to poor quality research that could simply have been skipped in exchange for simply making assumption. To once again quote Kahneman: “you do not believe that these results apply to you because they correspond to nothing in your subjective experience”.

We can fix these problems by instead putting our focus on research objectives. We pay attention to the reason that we are doing the research and work to understand if the results we get could help us with our objectives.

This does not get us off the hook however because we can still create poor research objectives.

Let’s look back at one of our prior hypothesis examples and try to find effective research objectives instead.

Example objectives: deciding on filters

In thinking about the colour filter we might imagine that this fits into a larger project where we are trying to decide what filters we should implement. This is decidedly different research to trying to decide what order to implement filters in or understand how they should work. In this case perhaps we have limited resources and just want to decide what to implement first.

A good approach would be quantitative research designed to produce some sort of ranking. But we should not dismiss qualitative research for this particular project – provided our assumptions are well defined.

Let’s consider this research objective: Understand how users might map their needs against the products that we offer . There are three key aspects to this objective:

• “Understand” is a common form of research objective and is a way that qualitative research can discover things that we cannot find with quant. If we don’t yet understand some user attitude or behaviour we cannot quantify it. By focusing our objective on understanding we are looking at uncovering unknowns.
• By using the word “might” we are not definitively stating that our research will reveal all of the ways that users think about their needs.
• Our focus is on understanding the users’ mental models. Then we are not designing for what users say that they want and we aren’t even designing for existing behaviour. Instead we are designing for some underlying need.

The next step is to look at the assumptions that we are making. One assumption is that mental models are roughly the same between most people. So even though different users may have different problems that for the most part people tend to think about solving problems with the same mental machinery. As we do more research we might discover that this assumption is not true and there are distinctly different kinds of behaviours. Perhaps we know what those are in advance and we can recruit our research participants in a way that covers those distinct behaviours.

Another assumption is that if we understand our users’ mental models that we will be able to design a solution that will work for most people. There are of course more assumptions we could map but this is a good start.

Now let’s look at another research objective: Understand why users choose particular filters . Again we are looking to understand something that we did not know before.

Perhaps we have some prior research that tells us what the biggest pain points are that our products solve. If we have an understanding of why certain filters are used we can think about how those motivations fit in with our existing knowledge.

Mapping objectives to our research plan

Our actual research will involve some form of asking questions and/or making observations. It’s important that we don’t simply forget about our research objectives and start writing questions. This leads to completing research and realising that you haven’t captured anything about some specific objective.

An important step is to explicitly write down all the assumptions that we are making in our research and to update those assumptions as we write our questions or instructions. These assumptions will help us frame our research plan and make sure that we are actually learning the things that we think we are learning. Consider even high level assumptions such as: a solution we design with these insights will lead to a better experience, or that a better experience is necessarily better for the user.

Once we have our main assumptions defined the next step is to break our research objective down further.

Breaking down our objectives

The best way to consider this breakdown is to think about what things we could learn that would contribute to meeting our research objective. Let’s consider one of the previous examples: Understand how users might map their needs against the products that we offer

We may have an assumption that users do in fact have some mental representation of their needs that align with the products they might purchase. An aspect of this research objective is to understand whether or not this true. So two sub-objectives may be to (1) understand why users actually buy these sorts of products (if at all), and (2) understand how users go about choosing which product to buy.

Next we might want to understand what our users needs actually are or if we already have research about this understand which particular needs apply to our research participants and why.

And finally we would want to understand what factors go into addressing a particular need. We may leave this open ended or even show participants attributes of the products and ask which ones address those needs and why.

Once we have a list of sub-objectives we could continue to drill down until we feel we’ve exhausted all the nuances. If we’re happy with our objectives the next step is to think about what responses (or observations) we would need in order to answer those objectives.

It’s still important that we ask open ended questions and see what our participants say unprompted. But we also don’t want our research to be so open that we never actually make any progress on our research objectives.

Reviewing our objectives and pilot studies

At the end it’s important to review every task, question, scenario, etc. and seeing which research objectives are being addressed. This is vital to make sure that your planning is worthwhile and that you haven’t missed anything.

If there’s time it’s also useful to run a pilot study and analyse the responses to see if they help to address your objectives.

Plan accordingly

It should be easy to see why research hypothesis are not suitable for most qualitative research. While it is possible to create suitable hypothesis it is more often than not going to lead to poor quality research. This is because hypothesis create the impression that qualitative research can find things that generalise to the entire user base. In general this is not true for the sample sizes typically used for qualitative research and also generally not the reason that we do qualitative research in the first place.

Instead we should focus on producing effective research objectives and making sure every part of our research plan maps to a suitable objective.

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User Research – The Importance of Hypotheses

It is easy to be tempted to look at the objective of your user research and pump out a solution that fits your best idea of how to achieve those objectives. That’s because experienced professionals can be quite good at that but then again they can also be very bad at it.

It is better to take your objectives and generate some hypothetical situations and then test those hypotheses with your users before turning them into concrete action. This gives you (and hopefully your clients) more confidence in your ideas or it highlights the need for changing those hypotheses because they don’t work in reality.

Let’s say that your objective is to create a network where people can access short (say a chapter) parts of a full text before they decide to buy the text or not. (Rather like Amazon does).

You can create some simple hypotheses around this objective in a few minutes brainstorming .

User-Attitude

We think that people would like to share their favourite clips with others on Facebook and Twitter.

User-Behaviour

We think that people will only share their favourite authors and books. They won’t share things that aren’t important to them.

User-Social Context

We think that people will be more likely to share their favourite authors and books if they are already popular with other users.

Why does this matter?

One of the things about design projects is that when you have a group of intelligent, able and enthusiastic developers, stakeholders , etc. that they all bring their own biases and understanding to the table when determining the objectives for a project. Those objectives may be completely sound but the only way to know this is to test those ideas with your users.

You cannot force a user to meet your objectives. You have to shape your objectives to what a user wants/needs to do with your product.

What happens to our product if our users don’t want to share their reading material with others? What if they feel that Facebook, Twitter, etc. are platforms where they want to share images and videos but not large amounts of text?

If you generate hypotheses for your user-research; you can test them at the relevant stage of research. The benefits include:

• Articulating a hypothesis makes it easy for your team to be sure that you’re testing the right thing.
• Articulating a hypothesis often guides us to a quick solution as to how to test that hypothesis.
• It is easy to communicate the results of your research against these hypotheses. For example:
• We thought people would want to share their favourite authors on social networks and they did.
• We believed that the popularity of an author would relate to their “sharability” but we found that most readers wanted to emphasize their own unique taste and are more likely to share obscure but moving works than those already in the public eye.

Header Image: Author/Copyright holder: Dave. Copyright terms and licence: CC BY-NC-ND 2.0

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What is UX Research: The Ultimate Guide for UX Researchers

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How to write and present effective UX research reports

Regardless of how thorough or valuable your user research is, it quickly becomes meaningless if you’re unable to succinctly put it together and present it in a digestible UX research report.

UX research reporting is a skill just as valuable as being able to conduct the research in the first place. It lets you showcase methodology and findings of your research, and ensure a product’s user experience delivers with the first iteration.

Luckily, how to write and present UX research reports is something you can learn. What’s more, this chapter will guide you through it (and provide free templates for your UX report).

What is a UX research report?

A user research report is an easy-to-digest summary of a user research project that aims to update product stakeholders on results, inform product decisions with user data, and harmoniously guide a product build or iteration.

Once upon a time, UX research reporting was a cumbersome, dreaded box to tick. It was notorious for resulting in unnavigable reports that product teams would rather leave at the bottom of their inbox than try to consume.

The word 'report' conjures images of lengthy word documents, a PDF one-pager, or hour-long presentation with an occasional GIF—but a research report doesn't have to mean that.

Kevin Rapley , Senior User Researcher at Justice Digital, explains a UX report as being “about arming our teammates with data that allows them to decide on the direction of a product or service.”

A useful UX report includes:

• The research goals and research process
• Research questions the report is hoping to answer
• A recap of the UX research plan
• What UX research methods were used and why
• Quantitative and qualitative data sets and conclusions
• Key insights & actionable takeaways
• An expanded data appendix

Why do you need a user research report?

Product teams need a user research report to reflect on research activities and accurately guide a product’s scope with key insights. A UX research report helps sort information, defend research, and affirm (or disprove) a hypothesis. No matter how well-organized your research repository is, sometimes simply having the research results available is not enough. A succinct report will align entire teams in one sitting by presenting findings in a unique way.

In short, a research report helps to:

• Positively influence UX design
• Make sense of data sets and explain complicated graphs or other quantitative research results
• Provide actionable recommendations on next steps
• Summarize key findings, so they can be translated into every role and responsibility of the product team

Where UX research enables product teams to understand the user, prove or disprove hypotheses, and prioritize and generate ideas, a UX research report ensures the user is at the center of every product decision. Presenting that UX report then aligns team members on goals and priorities, and provides authentic user insights to inform every product decision.

We’ve covered what a research report is , but what is it not ? A UX research report is not a static, one-time document that your team reads once. It’s an ongoing reference point; the guardrails and guiding insights that guarantees the entire build stays on track.

How to write an effective UX research report: the essential elements

No matter how you choose to present your research study, there are a few elements that every report needs to include for it to be both useful and effective. Let’s look at how to create a report.

Introduction

Your introduction should lay out your research goals, plan, and scope. It should cover your product team’s pain points, and give a clear study overview. You need to answer what you did and why. The introduction can go on to include sales support data and competitive product analysis that inspired or guided this research project.

It’s a good idea to set up how this research helps to support and answer related company goals, team-level goals, and product-dev goals: so all stakeholders know it’s got something for them.

You can include questions from your UX research strategy you had originally hoped to answer, even if your results have gone on to answer other questions as well. Now’s also a good time to introduce research stakeholders: your fellow research team members.

As a secondary step to your introduction, ensure you’re including the approach you took to your UX research process : i.e. what research methods you used, as well as participant profiles and your user personas .

Don’t feel you need to spend too much time on this, says Charlie Herbozo Vidal , Senior User Experience Researcher at CVS Health. “As researchers, it’s not uncommon to dwell on the methodology for longer than needed. While interrogating methods might be valuable to other researchers, business partners might be disengaged by them.”

Ultimately, while methodology is important, it’s the results that most people are here for.

Key findings

This is where you get people on the edge of their seats! Give an overview of your findings, before breaking them down into more detail. Remind your audience ‘what we thought’ vs. what you actually learned.

Artifacts to use are:

• User personas built
• Insights from customer interviews
• User journey maps
• Prototype testing
• Storyboards
• Feedback & satisfaction reports

At the end of this section, and continuing throughout your presentation, you can pepper relevant atomic research nuggets.

Make sure you champion the user's needs throughout, and make special notes of 'offhand' comments users make. Often, it's the random comments that provide the most insight—they must not be forgotten about when writing the report.

Jack Dyer , Designer at Interactive Schools

Summarize your quantitative and qualitative research , and how they’ll both impact your product design and growth. Lay out opportunities versus risks, good-to-knows versus must-knows. Here you’ll want to convey the impact of each suggested step, roadmap designs, and figure out the long and short-term project scope. A few things to cover in your next steps are:

• Long and short-term goals
• ICE framework (Impact, confidence, ease)
• Roles and responsibilities for each task
• A timeline of events and project map
• A request for resources
• Desired outcomes

No matter how you’re presenting your research, be it asynchronously or not, you’ll need to include a Q&A. These can be subjective (based on what you think your team is likely to ask), pre-collected ahead of the presentation, answered live, or an opportunity to build an FAQ later.

What’s important is to acknowledge and be open to receiving questions. After all, questions are a positive thing—it means people are actually listening!

It’s easy to overlook the appendix after putting together a detailed report, but all that glorious research data needs to be accounted for and referenced clearly. Plus, you never know to what extent your team will want to dive into it. Your appendix is also where you’ll want to include secondary research that didn’t make the cut but backs up your research.

9 ways to present UX research findings

UX research reporting will look a little different depending on your internal personas and organizational culture. First, ask yourself: who is your audience? Who needs to see the report, and who will benefit from seeing it? This will help determine how to present your user research report.

A few things to consider:

• Are you working with internal or external stakeholders? Tool limitation and file-sharing will differ for both.
• Are you working with an in-office, fully-remote, or hybrid team?
• Are you sitting in the same time zone or not?
• What are the knowledge levels like within your team?
• How does your team communicate daily/weekly/monthly?
• Are there any predetermined knowledge bases or tools your team is comfortable with?

The most common players across a UX team that need to understand your UX research report are:

• Product Designers (UX/UI)
• Fellow Product Researchers
• UX/UI Writers

However , it doesn’t stop at your product decision-making team. More often than not, there will be other stakeholders that can benefit from your research presentation. Your marketing, finance, sales, and even C-suite executives will massively benefit from your findings too. If you can tailor versions of your report or provide key summaries for each collective, even better!

Psst 👉 This is much easier to do when you have a research team that can host stakeholder interviews ahead of your research process.

Now, let’s get into the report formats to consider:

1. Workshops: for real-time, collaborative reports

First up, workshops. Workshops are a unique way of keeping your report interactive and engaging. They can be held remotely or in-person, but are almost impossible to hold asynchronously—so time zones are a big factor here.

You’ll also want to consider workshopping tools if you’re hosting digitally—a few to consider are: Miro, Mural, FigJam, and Gather.

A plus with workshops is that your stakeholders will actively have a say early on in the product development process , allowing you to foster more diverse inputs, minimize research bias you may have accumulated in your summaries, and build a sense of responsibility for the product’s success early on.

A negative of workshops is that they can be culprits to in-the-room or bandwagon bias. People are quick to ride on the coattails of a strong conclusion, without fully understanding or trialing another (less popular) conclusion or suggestion.

2. Slack channels: for an asynchronous and interactive research repository

Slack is a great option (especially if you’re already using it) if your research team needs to deliver insights to a fully-distributed collection of stakeholders. Slack tends to be the go-to channel for startups and creative companies, and there’s some key features you can tap into:

• Canvas: Store files, images, videos, and more in one place
• Huddles: Jump on a quick chat to fill in any gaps
• Clips: Post audio, video, or screen-sharing clips
• Connect: Team up with freelancers and agencies working on the project with you
• Workflow: Build drag-and-drop processes from your findings
• Knowledge sharing: Tag your data accordingly so it's easy to find later

3. Knowledge bases: for self-serve UX research reports

Knowledge bases can be a great home for your research presentation, and work especially well for distributed teams working across different time zones.

However your team is set up, research repositories are incredibly valuable. Sharing your report in a centralized location, regardless of the other ways you distribute findings, can democratize research , showcase the impact of your work, and disseminate valuable insights throughout your entire organization.

Keep in mind that knowledge bases can be tough to navigate if poorly organized or tagged. If you’re storing your UX research report in a knowledge base, ensure you provide clear instructions on how someone can find it, and how to navigate through the report itself.

If you have the time, run a card sorting test with an internal focus group to see how you can logically sort your research for those who are going to be looking for it.

4. Presentations / slide decks: great for the PAS framework

Live presentations tend to be the most impactful, but do risk being short-lived if you don’t have a follow-up plan for after your presentation.

While they’re great for sharing metrics and visuals, and can provide a beautiful overview of your research project, presentations can be a little one-sided. This one-way presentation style can prevent collaboration and innovation from the rest of the team. Consider how you can make your presentation interactive or engaging, whether it’s taking questions throughout or doing a ‘choose your own adventure’ session and asking people which sections they want to review first.

Kevin Rapley , Senior User Researcher at Justice Digital, recommends presenting slides using the PAS framework:

• Problem: State the problem you set out to overcome
• Agitate: Detail the impact or opportunity missed by not meeting the problem
• Solution: Offer a route forward from the research findings and insights, the next steps, and likely outcomes by solving the problem

Kevin explains that the PAS framework cuts to the detail people are invested in: “Stakeholders want to know the path forward: Are we on the right track to build this service? Have we uncovered user engagement or uptake issues? Have we learned that our assumptions are incorrect and we now have a better understanding of user needs? Presenting slides in this way delivers what’s needed.”

5. Written reports: for direct and simple sharing

If it’s not broken, don’t fix it. A written report is probably the idea that jumped to the front of your mind when you read the title of this chapter. For many, this may seem like the ‘OG’ of UX reports.

These types of reports often come as a PDF or a word document, making them static, reluctant to change, and resulting in low engagement or re-reads. Delivering a written report via email also means you can’t guarantee your audience is going to read it. On the other hand, written reports can be incredibly detailed, scanable for different stakeholders, and include all kinds of results from visual data to qualitative findings.

For many teams this method still works, especially if you’re trying to communicate findings to a distributed, asynchronous team. Written UX reports enable people to go through things in their own time—and come back to it when they need to.

6. Atomic research nuggets: to eliminate ‘bad research memory’

Deriving from an atom—the smallest unit of matter—atomic UX research nuggets are minute and succinct conclusions from data points. They’re always aligned and tagged with a product direction. Formalized by Tomer Sharon and Daniel Pidcock , it’s described as “the concept of breaking UX research down into its constituent parts”:

Experiments: “We did this…” Facts: “…and we found out this…” Insights: “…which makes us think this…” Conclusions: “…so we’ll do that.”

Atomic research nuggets help to fight ‘bad research memory’—the idea that knowledge gets lost or forgotten amid the depths of a larger report. These nuggets are accessible, usable, and searchable. They can be delivered (or accessed) throughout an entire product build, serving as North Stars for micro goals. Research nuggets can be a firm reminder your team is, or isn’t, taking the right action.

7. Pre-recorded video: for better knowledge retention

People retain 90% of the information they receive via video versus text. There’s no question that, for many, video is a better way of onboarding and remembering information. At the same time, it can be easier to share information via video if your UX researchers aren’t the most confident of writers.

Although pre-recorded video is an easy way to share a UX research report with a team, as with other formats on this list, you’ll need to ensure people actually watch it.

Loom can be a great screen-sharing video recording tool. Some of their features and paid plans will enable you to see who from your team has watched your video, as well as spark conversation and engagement opportunities throughout the video. Alternatively, you can share the video as a watch-along during a synchronous meeting and discuss afterwards, while still sharing the video with those who can’t attend live.

8. Case studies: for sticky storytelling

Case studies aren’t just for winning potential customers. At their very core, case studies are put together to convince someone of something due to a real-life story. This is why they can be great if your UX research report needs to convince a diverse or largely cautious selection of stakeholders.

What’s more, case studies tend to rely on storytelling tactics and a strong narrative to get their point across. They can pull from user personas to further a point and make it more relatable for your design team. Muhammad Ahmad , UX Designer at VentureDive, shares the value presenting reports as case studies holds:

“Case studies show how you think. As a UX Researcher or Designer, how you percieve problems and what framework you use to evaluate them matters a lot. Your case studies are supposed to show just that.”

9. Maze reports: for all-in-one research and reporting

Automate your reporting with Maze. Maze automatically generates a report for each test you run, turning it into an easy-to-navigate dashboard. Add comments to generate conversation, highlight key responses and generate usability scores for your prototype testing .

If you’re working with moderated research, Maze AI can speed up the reporting process with automated sentiment tagging, project naming, and even generating summaries and identifying critical learnings from user interviews . So you can sit back, and let Maze take care of the data processing.

When you’re happy with your report, generate a custom link that you can share with your team, and further internal and external stakeholders.

Using Maze reports will enable you to share:

• Introduction and mission slides
• An analysis of each UX research method: From card sorting to live website testing
• In-depth breakdowns of research data
• Overviews of the report metrics: From misclicks to bounce rates and time-on-screen
• A usability score

These reports will also allow you to download CSV files of your data, and customzie filters and views to bring your stakeholders the numbers they need, fast. Your team will be able to collaborate in a comments section and let AI identify key themes and takeaways if you’re struggling to spot them.

Overall, UX research tools with in-built reporting are a great way to translate and share all of your research into visual data sets that can be digested by the rest of the team in a few clicks.

7 UX research report templates

There are some fantastic research report templates to help get you started on your journey. Here are some of our favorites to help you better present those deliverables, key learnings, and everything in between.

Maze: Usability testing report

Hosted on Pitch, this report template is clear, simple, and follows a lot of the design and framework best practices shared in this chapter.

Access the template here

Aadil Khan: UXR report with examples

A straightforward report template is designed by Aadil Khan , UX Researcher at IBM, who says: “I made this template based on tons of mentoring calls I’ve been in with people looking to land UXR jobs where we discuss how to present UXR case studies during interviews and such. Oftentimes their case studies were too lengthy and lacked some sort of narrative structure to make it easier to present.”

EaTemp: Key findings report

A beautifully-designed template hosted on Figma. Get access to personas, empathy maps, and card sorting. All colors, fonts, and shapes are customizable.

Miro: Research repository template

Build a centralized research hub on Miro. Connect your team in a few clicks and allow them to collaborate with this free template. Note: you’ll need to sign up for a (free) Miro account.

Furquan Ahmad: UX research report template

A sleek and vibrant presentation, this template was created by Furquan Ahmad , Product Designer at Meta, “to help people focus their energy and time on the insights they're providing rather than worry about what the presentation will look like. I'm always shocked at how many people have benefited from the community.”

Estefanía Montaña Buitrago: Atomic UX research canvas

Beautifully designed on FigJam, this canvas by Estefanía Montaña Buitrago , UX Designer at Globant, has been used by over 7,000 people and now comes with several useful remixes too.

Muhammad Ahmad: UX research kit

Muhammad Ahmad , UX Designer at VentureDive, shared this minimalistic template. Here you’ll get 60+ customizable templates in both light and dark modes. There’s a free version, or a (paid) premium version which may be worth the investment for you.

Best practices for writing an effective UX research report

The functionality of your research report will come down to how you write it. Sitting down and being faced with copious amounts of data can make UX reporting feel like a daunting task—here’s some techniques and tips to help you along the way.

Take a leaf from your UX design book with user-friendly copy

No matter the format, you want your UX report to be as accessible and skim-able as possible for your audience. It’s a good idea to mimic some of the same mentalities you would use in UX design.

Gestalt grouping principles are good to consider for UX report writing. Think similarity , proximity , and common-region for grouping relevant information.

Similarly, UI design principles such as the figure-ground and focal point will help direct your readers’ eyes to the most important information first, as well as make for a more accessible read.

Lastly, Gestalt’s continuity principle is a great one to apply to your UX report. Readers naturally follow patterns for easier flows in information, so if you’re including stylistic elements like bolding, italics, asides, indenting, or something else, ensure these run consistently throughout your report.

At the same time, think about the structure, layout, and formatting of your written report. Are you leaving enough negative space for your reader to process information? These are especially important for readers with dyslexia, but will generally lift your readability on the whole:

• Is all of your copy aligned left?
• Is your font choice clear with a good amount of spacing between letters and words?
• Are you bolding important words and sentences rather than underlining them?
• Are you peppering your report with enough headings and subheadings?

Release oxytocin: Follow storytelling tactics

A Forbes article reported that “immersive storytelling releases the empathy-related chemical oxytocin in our brains.” If you’re not familiar with oxytocin, it’s known as a natural ‘feel-good’ chemical, promoting feelings of trust and attachment.

Why else do you think case studies are so effective? They rely on storytelling: they have characters, plots, beginnings, endings, peaks, and pits. User research reports that mimic storytelling threads and tactics are more likely to create sticky data points, as well as hold your readers’ attention throughout. This is why the PAS framework works so well, but whatever format your report takes, bear in mind a story-like structure with a beginning, middle, and end.

Ask your editor to edit your research presentation with the three Cs in mind

Clear , Concise , Compelling . These core principles exist everywhere the written word does, but it can be hard to spot them when editing your own work. Just because something is clear, concise, and compelling for you, doesn’t mean it is for someone else—ask a colleague to read your report (or, better yet—a content editor).

Failing that, if you don’t have access to an editor or are in a time crunch, here are some tools to help you edit your own work.

• Grammarly: Good for catching those little typos and grammatical errors
• Hemingway Editor: Gives a readability score and helps to simplify sentences

Consider your reader, and rethink the jargon

Tailoring your report to meet the needs and knowledge level of each stakeholder is a balancing act. Many will tell you to avoid jargon, acronyms, and technical language at all costs. But, that’s not always the case. Sometimes, using industry jargon is the most direct way of getting your point across, and if you know your reader understands it, go for it.

However, keep in mind that if your report is going to other teams: sales, C-suite, finance, etc, then you may need to find alternative terms that aren’t department-specific—or provide a glossary or acronym dictionary within the report.

Muhammad shares more: “Typically UX folks (or even product folks) are not that well-equipped with research terminologies. So giving them the summary of the research in plain language is the approach that works best for me.”

Wrapping up how to present user research findings

There you have it, a complete guide on how you can write and present your user experience research in a way that everyone can benefit from it.

Remember, be conscious of your audience, your format, and your language. Different stakeholders and team cultures require different reporting styles, it’s up to you to curate the information into a report that delivers the insights you’ve uncovered.

Generate UX reports that have impact

From AI-generated summaries of your user interviews, to usability scores for your prototype tests, automate UX research reporting with Maze.

Finish reading

5 rules for creating a good research hypothesis

UserTesting

A hypothesis is a proposed explanation made on the basis of limited evidence. It is the starting point for further investigation of something that peaks your curiosity.

A good hypothesis is critical to creating a measurable study with successful outcomes. Without one, you’re stumbling through the fog and merely guessing which direction to travel in. It’s an especially critical step in  A/B and Multivariate  testing. 

Every user research study needs clear goals and objectives, and a hypothesis is essential for this to happen. Writing a good hypothesis looks like this:

1: Problem : Think about the problem you’re trying to solve and what you know about it.

2: Question : Consider which questions you want to answer. 

3: Hypothesis : Write your research hypothesis.

4: Goal : State one or two SMART goals for your project (specific, measurable, achievable, relevant, time-bound).

5: Objective : Draft a measurable objective that aligns directly with each goal.

In this article, we will focus on writing your hypothesis.

Five rules for a good hypothesis

1: A hypothesis is your best guess about what will happen. A good hypothesis says, "this change will result in this outcome. The "change" meaning a variation of an element. For example manipulating the label, color, text, etc. The "outcome" is the measure of success or the metric—such as click-through rate, conversion, etc.

2: Your hypothesis may be wrong—just learn from it. The initial hypothesis might be quite bold, such as “Variation B will result in 40% conversion over variation A”. If the conversion uptick is only 35% then your hypothesis is false. But you can still learn from it. 

3: It must be specific. Explicitly stating values are important. Be bold, but not unrealistic. You must believe that what you suggest is indeed possible. When possible, be specific and assign numeric values to your predictions.

4: It must be measurable. The hypothesis must lead to concrete success metrics for the key measure. If you choose to evaluate click through, then measure clicks. If looking for conversion, then measure conversion, even if on a subsequent page. If measuring both, state in the study design which is more important, click through or conversion.

5: It should be repeatable. With a good hypothesis you should be able to run multiple different experiments that test different variants. And when retesting these variants, you should get the same results. If you find that your results are inconsistent, then revaluate prior versions and try a different direction. 

How to structure your hypothesis

Any good hypothesis has two key parts, the variant and the result. 

First, state which variant will be affected. Only state one (A, B ,or C) or the recipe if multivariate (A & B). Be sure that you’ve recorded each version of variant testing in your documentation for clarity. Also, ensure to include detailed descriptions of flows or processes for the purpose of re-testing.

Next,   state the expected outcome. “Variant B will result in a 40% higher rate of course completion.” After the hypothesis, be sure to specifically document the metric that will measure the result - in this case, completion. Leave no ambiguity in your metric. 

Remember, always use a "control" when testing. The control is a factor that will not change during testing. It will be used as a benchmark to compare the results of the variants. The control is generally the current design in use. 

A good hypothesis begins with data. Whether the data is from web analytics, user research, competitive analyses, or your gut, a hypothesis should start with data you want to better understand.

It should make sense, be easy to read without ambiguity, and be based on reality rather than pie-in-the-sky thinking or simply shooting for a company KPI or objectives and key results (OKR). 

The data that results from a hypothesis is incremental and yields small insights to be built over time. 

Hypothesis example

Imagine you are an eccomerce website trying to better understand your customer's journey. Based on data and insights gathered, you noticed that many website visitors are struggling to locate the checkout button at the end of their journey. You find that 30% of visitors abandon the site with items still in the cart. 

You are trying to understand whether changing the checkout icon on your site will increase checkout completion. 

The shopping bag icon is variant A, the shopping cart icon is variant B, and the checkmark is the control (the current icon you are using on your website). 

Hypothesis: The shopping cart icon (variant B) will increase checkout completion by 15%. 

After exposing users to 3 different versions of the site, with the 3 different checkout icons. The data shows... 

• 55% of visitors shown the checkmark (control), completed their checkout. 
• 70% of visitors shown the shopping bag icon (variant A), completed their checkout. 
• 73% of visitors shown the shopping cart icon (variant B), completed their checkout.

The results shows evidence that a change in the icon led to an increase in checkout completion. Now we can take these insights further with statistical testing to see if these differences are statistically significant . Variant B was greater than our control by 18%, but is that difference significant enough to completely abandon the checkmark? Variant A and B both showed an increase, but which is better between the two? This is the beginning of optimizing our site for a seamless customer journey.

Quick tips for creating a good hypothesis

• Keep it short—just one clear sentence
• State the variant you believe will “win”  (include screenshots in your doc background)
• State the metric that will define your winner  (a download, purchase, sign-up … )
• Avoid adding  attitudinal  metrics with words like  “because”  or  “since”  
• Always use a control to measure against your variant

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

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).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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See an example

Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

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 .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Prevent plagiarism. Run a free check.

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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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.

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Hypothesis Testing in the User Experience

It’s something we all have completed and if you have kids might see each year at the school science fair.

• Does an expensive baseball travel farther than a cheaper one?
• Which melts an ice block quicker, salt water or tap water?
• Does changing the amount of vinegar affect the color when dying Easter eggs?

While the science project might be relegated to the halls of elementary schools or your fading childhood memory, it provides an important lesson for improving the user experience.

The science project provides us with a template for designing a better user experience. Form a clear hypothesis, identify metrics, and collect data to see if there is evidence to refute or confirm it. Hypothesis testing is at the heart of modern statistical thinking and a core part of the Lean methodology .

Instead of approaching design decisions with pure instinct and arguments in conference rooms, form a testable statement, invite users, define metrics, collect data and draw a conclusion.

• Does requiring the user to double enter an email result result in more valid email addresses?
• Will labels on the top of form fields or the left of form fields reduce the time to complete the form?
• Does requiring the last four digits of your Social Security Number improve application rates over asking for a full SSN?
• Do users have more trust in the website if we include the McAfee security symbol or the Verisign symbol ?
• Do more users make purchases if the checkout button is blue or red?
• Does a single long form generate higher form submissions than the division of the form on three smaller pages?
• Will users find items faster using mega menu navigation or standard drop-down navigation?
• Does the number of monthly invoices a small business sends affect which payment solution they prefer?
• Do mobile users prefer to download an app to shop for furniture or use the website?

Each of the above questions is both testable and represents real examples. It’s best to have as specific a hypothesis as possible and isolate the variable of interest. Many of these hypotheses can be tested with a simple A/B test , unmoderated usability test , survey or some combination of them all .

Even before you collect any data, there is an immediate benefit gained from forming hypotheses. It forces you and your team to think through the assumptions in your designs and business decisions. For example, many registration systems require users to enter their email address twice. If an email address is wrong, in many cases a company has no communication with a prospective customer.

Requiring two email fields would presumably reduce the number of mistyped email addresses. But just like legislation can have unintended consequences, so do rules in the user interface. Do users just copy and paste their email thus negating the double fields? If you then disable the pasting of email addresses into the field, does this lead to more form abandonment and less overall customers?

With a clear hypothesis to test, the next step involves identifying metrics that help quantify the experience . Like most tests, you can use a simple binary metric (yes/no, pass/fail, convert/didn’t convert). For example, you could collect how many users registered using the double email vs. the single email form, how many submitted using the last four numbers of their SSN vs. the full SSN, and how many found an item with the mega menu vs. the standard menu.

Binary metrics are simple, but they usually can’t fully describe the experience. This is why we routinely collect multiple metrics, both performance and attitudinal. You can measure the time it takes users to submit alternate versions of the forms, or the time it takes to find items using different menus. Rating scales and forced ranking questions are good ways of measuring preferences for downloading apps or choosing a payment solution.

With a clear research hypothesis and some appropriate metrics, the next steps involve collecting data from the right users and analyzing the data statistically to test the hypothesis. Technically we rework our research hypothesis into what’s called the Null Hypothesis, then look for evidence against the Null Hypothesis, usually in the form of the p-value . This is of course a much larger topic we cover in Quantifying the User Experience .

While the process of subjecting data to statistical analysis intimidates many designers and researchers (recalling those school memories again), remember that the hardest and most important part is working with a good testable hypothesis. It takes practice to convert fuzzy business questions into testable hypotheses. Once you’ve got that down, the rest is mechanics that we can help with.

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• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

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).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 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 .

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.

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).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 18 September 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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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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User Research: What Is It and How to Do It in SaaS

12 min read

Looking for the best ways to conduct user research and gather actionable insights?

Whether you’re building a product from scratch, updating something on your platform, or just want to listen to users and create better experiences , this article provides the guide you need.

We covered:

• The benefits of proper user research.
• A detailed 5-step process for conducting effective research.
• Types of user research and different methods to implement.
• User research employs various qualitative and quantitative methods to investigate and understand users better. It helps you create a user-centered design process and ensure your final product is what customers love.

Effective user research helps you:

• Understand user behaviors, needs , and preferences.
• Identify experience gaps and remove friction.
• Increase product value and improve user experience .

Implement this user research process to gather data that’s exhaustive and actionable:

• Define your main objective and build a hypothesis.
• Choose research participants that represent your target audience.
• Choose the appropriate research method .
• Start collecting data.
• Analyze and form a conclusion.

User research methods for SaaS:

Usability testing

User testing, user interviews, focus groups, session replays, first click testing, user feedback surveys, card sorting, a/b testing, product analytics.

• Userpilot can help you conduct user experience (UX) research and easily interpret the data. Book a demo to discuss your needs with our team and get tailored solutions.

What is user research?

User research employs various qualitative and quantitative methods to investigate and understand users.

It’s a critical part of the product development process that helps inform design decisions and ensure the final product aligns with user expectations.

Why is conducting user research important?

Without effective user research, you’ll be building or updating your product based on assumptions, and that’s not a good place to be.

It’s like trying to construct a bridge without ever stepping onto the riverbank. You might craft something impressive, but without understanding the water’s flow, depth, and potential hazards, your bridge risks being unusable or, worse, dangerous.

Understand user behaviors, needs, and preferences

By conducting proper user research, you’ll gain data showing how users interact with your tool and their underlying needs and motivations.

This knowledge forms the foundation for designing products that resonate with users. It ensures what you build not only meets functional requirements but also aligns with the specific desires of your target audience.

Identify experience gaps and remove friction

An experience gap refers to the disparity between users’ expectations and their actual experience with your product. Such gaps can lead to customer dissatisfaction, as user needs are not adequately met.

User experience research is pivotal in closing experience gaps and removing friction from the user journey. By identifying pain points through methods like usability testing and feedback collection, you can pinpoint areas for improvement.

This proactive approach allows you to implement targeted enhancements, ensuring a smoother and more satisfying user experience. Ultimately, addressing these gaps not only boosts user satisfaction but also cultivates customer loyalty and positive brand perception.

Increase product value and improve user experience

Continuous user experience research ensures your product keeps adding more value and enhancing the user experience. With this, users will be more comfortable interacting with your product regularly.

As they incorporate your tool into their workflows and increase engagement, they’ll have more reasons to expand their accounts, leading to higher revenue for your business.

How to conduct user research to improve user experience

Ready to start conducting user research? Here’s a five-step process to follow:

1. Define your main objective and build a hypothesis

Before diving into user research, clearly define your primary objective. Whether it’s improving onboarding processes , enhancing navigation, or refining a specific feature, having a focused goal is crucial.

Use the objective to create a hypothesis of the results you hope to get. The research will then confirm or reject this hypothesis.

For instance, if your objective is to enhance your app’s usability, a hypothesis might be that simplifying the navigation will lead to a higher user satisfaction score . You can then design user feedback surveys to collect user opinions and see if your hypothesis is correct.

2. Choose your research participants

Identifying the right participants is key to obtaining relevant insights. Define segments based on characteristics like user type (current users, new users), demographics, or usage patterns.

For example, if you’re improving a feature specific to premium users, draw research participants from users who have engaged with the feature enough to provide valuable feedback. Taking this targeted approach ensures the data you obtain is relevant and actionable.

3. Choose the appropriate research method

There are many user research methods, but what you use generally depends on your objective. For example:

• Usability testing : Helps assess how easily users can accomplish specific tasks within the app.
• Features heatmap : Visually highlights user interaction with specific elements or features of your tool.
• First click testing : Focuses on the first click users make, revealing initial impressions and navigational challenges.
• User feedback surveys : Collects user opinions, preferences, and suggestions, providing valuable qualitative data.
• Card sorting : Helps understand how users categorize information, aiding in intuitive information architecture and a user-centered design process.

Depending on your objectives, you can employ several other types of user research methods. We’ll provide an extensive list in a later section.

4. Start collecting data

After deciding on your objective and choosing a suitable user research approach, it’s time to execute and gather valuable data.

Ensure you have the necessary resources (user research tools, participants, and the like) and clearly define the steps for data collection.

For instance, let’s return to the example we discussed in step 1. Recall the objective was to enhance app usability, and the user research technique was customer feedback surveys. Now that you have those two settled, it’s time to begin collecting data.

You want to keep the survey short and concise to get the best result. Combine various question types, including multiple-choice, open-ended, and rating scales. This provides a more comprehensive view of user opinions and allows for both quantitative and qualitative user research.

For example, you can ask: “ How would you rate the overall usability of [your app] on a scale of 1 to 5, where 1 is very poor, and 5 is excellent? ” and follow up with, “ Please share any specific challenges or difficulties you encountered while using the app, and if you have any suggestions for improving the usability, feel free to provide them here .”

Imagine your objective is broader—you want to understand usability, then decide which of two features to prioritize for an update. After the initial survey, you can ask what feature they prefer and the reason for their choice. Make a decision based on the data you obtain.

5. Analyze and form a conclusion

Once data collection is complete, the next step is to analyze the gathered information. Bring together all the data you’ve collected and form a comprehensive understanding of user behavior. Identify patterns, trends , and pain points within the data.

When you’re done, it’s time to implement changes to improve the user experience. For instance, imagine your research data shows your onboarding takes too long and results in drop-offs due to several unnecessary steps.

Your product team can work on identifying areas of the onboarding flow they can cut off. Also, you can implement an onboarding checklist to reduce the time to value and boost adoption rates.

Types of user research

User research is quite broad, but when you look at it closely, anybody researching users is either implementing quantitative or qualitative methods—or both.

Quantitative research

This user research type involves collecting numerical data to measure and analyze specific aspects of user behavior and preferences.

It uses surveys, analytics, and A/B testing to uncover user data.

Qualitative research

While quantitative research asks the what questions, qualitative research focuses on uncovering the why behind user behavior . For example, realizing that users are dissatisfied with a new feature is just the first step in your research process. You still don’t have sufficient data to make the changes your users will love.

But by conducting research that asks users why they don’t like the feature, you can identify changes to make. Examples of qualitative research methods include interviews, focus groups, and open-ended user feedback surveys.

User research can be both attitudinal and behavioral:

• Attitudinal research helps uncover user attitudes, opinions, and emotional responses to your product.
• Behavioral research focuses on observing and analyzing actual human behavior and interactions with your product.

User research methods

Combine any of the following qualitative and quantitative research methods to collect comprehensive user data and make informed development decisions:

Usability tests involve observing users as they interact with a prototype or an existing product to identify challenges and assess the overall user experience. You can do this remotely using specialized usability testing tools or have testers come together in a physical test lab while a user researcher observes and records everything.

You can also implement think-aloud protocols, asking users to verbalize their thoughts while interacting with the product.

During the test, aim to identify how well your product performs against these usability components :

• Learnability : The ease with which users can understand and navigate your product for the first time.
• Efficiency : User speed and effectiveness in performing tasks—this says a lot about your UI.
• Memorability : The extent to which users can remember how to use the product or feature after an initial encounter. Good memorability is a sign of a reduced learning curve.
• Errors : The frequency and severity of user mistakes while interacting with your product. Too many test participants making errors is a sign of friction.
• Satisfaction : The overall fulfillment and positive sentiment users experience when interacting with the product.

User testing and usability testing sound similar; people even use them interchangeably, but they’re not the same.

While the former is focused on evaluating functionality and ease of use , user or UX testing encompasses a broader spectrum, digging deep into user needs and preferences . Another way to put it is that usability testing is a subset of UX testing.

The specific approach you adopt when testing users depends on your research objectives, but just like any user research approach, you begin by deciding what feature, product, or prototype to test. Then, create the test task with a list of objectives and have it done remotely or in person.

Example of UX testing: create different interface designs , then ask users to interact with them and mention the one they find most appealing. Implement task analysis to analyze the data and uncover user user preferences.

If it were to be a usability test, you’d create a prototype and ask users to accomplish a specific task with it—e.g., schedule social media posts—then observe the steps they follow and how long it takes.

User interviews involve one-on-one conversations between you and participants to gather in-depth qualitative insights into their experiences and collect relevant data.

Although it can be more tasking than a quick usability test, a user interview allows you to collect extensive data and get immediate responses to your follow-up questions.

The best way to conduct interviews for your SaaS is over video conferencing platforms like Zoom—the one-on-one interaction allows for easy communication.

Here’s an interview preparation template you can use when preparing to interview users:

This is a structured group research involving a small group of 6-12 users (you can do more if you have the resources). Usually, an experienced moderator is present to facilitate discussions and debates about your product.

While the discussion is ongoing, someone is recording user thoughts, opinions, and attitudes toward the topics raised. In the end, you’ll gather useful qualitative data from different participants and use it to advise your product design process.

Side note: you can also use focus groups if you’re conducting market research as part of your development process.

This method involves using tools like Hotjar to record and analyze user sessions on your website or app.

By viewing clicks, scrolls, and keystrokes in a natural environment without users knowing someone is recording, you’ll gather quantitative data on click patterns and session duration, among other things. You can analyze the results to identify if users follow your tool’s happy path and see how they respond to your interface.

The first click test is an incredibly important component of user research. When users make the right first click, they’re more likely to achieve their goals faster and be satisfied with your tool than when they click several times on the wrong UI elements before finding the happy path.

First click testing helps you determine if your product is intuitive—and if it isn’t, you’ll see the errors users make and know what changes to implement.

To conduct this test, show users a mockup, screenshot, or prototype of your tool and ask them to verbally share their initial click choice and reasoning.

You can also have an interactive test where you share the task with users and have them click on what they think should be the first step. That’s what user researchers did in the example below:

They presented users with Bank of America’s homepage and tested to see where users click to find information. 82% of the test participants went to the right section of the homepage, demonstrating an intuitive design.

From quick quantitative questions to more in-depth qualitative research, user feedback surveys come in different forms.

The specific survey type you use depends on your objective. For instance, if you need to understand the ease of using your platform, trigger a customer effort score survey asking users to rate how much effort they put into using specific features. Other common survey types you might want to implement include NPS and CSAT surveys.

Userpilot can help you create in-app surveys, decide who sees them, and analyze the results quickly. Here’s what building your surveys with our tool looks like:

This research method comes in handy when testing your information architecture. Card sorting involves giving test participants cards representing various features, functions, or sections of your SaaS. For example, the cards might include “dashboard,” “reports,” “settings,” and so on.

You can ask participants to categorize the cards into predefined groups or tell them to do it as they deem fit. Choose the former if you already have a structure you wouldn’t want to change.

Take note of participants’ grouping patterns and any challenges or uncertainties they may encounter in the process. Once you’re done, implement task analysis to interpret the result and make data-driven decisions.

A/B testing compares two versions (A and B) of a webpage, email, or feature to determine which performs better in terms of user engagement or conversion.

With a tool like Userpilot, you can create different versions of the in-app flows or UI elements you want to test, and then run them through specific user segments. Userpilot also allows you to conduct multivariate tests where you compare more than two variables.

Product analytics involves collecting and analyzing data from user interactions with your platform to understand their behavior and preferences.

Userpilot’s robust analytics platform lets you track user actions extensively and generate different analytics reports to identify trends, patterns, and changes in user behavior. What’s more, you can visualize the results in a detailed analytics dashboard for easy interpretation and decision-making.

Heatmaps provide visual representations of user behavior. They’re generated based on data from user interactions, such as clicks, scrolls, or mouse movements, recorded during user sessions on a website or app.

As in the image below, heatmap tools assign colors ranging from warm to cool tones to demonstrate different engagement levels. Hotter colors (e.g., red) indicate high interaction, while cooler colors (e.g., blue) represent lower or no interaction.

Userpilot allows you to select the features of your product you want to track and generate real-time heatmap reports to see how users interact with your tool. This is useful when you want to make quick decisions about what features receive better engagement.

User research always pays off.

When you invest in understanding user needs, expectations, and pain points, you’ll build an exceptional user experience that drives retention and loyalty.

That’s not to mention the fact that your product will stay competitive, making it easy to expand your user base and offering.

Ready to start reaping these benefits? Book a demo now and see how Userpilot can help you implement different user research methods and easily interpret the results.

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

Perfect Your Manuscript With Professional Editing

Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

Ben Holliday

Hypotheses in user research and discovery.

Back in 2015 I wrote ‘ Everything is hypothesis-driven design ‘. It remains one of my most (and still frequently) read blog posts.

Something I’ve been thinking about more recently is how this translates to user research and ‘discovery’.

A discovery should be focussed on research and learning. Helping a team or organisation challenge their existing ideas, knowledge and understanding of a problem space. I believe that research hypotheses, based on an understanding of assumptions, are a great way to frame and organise this work.

Starting with testable assumptions

Hypotheses can be thought of as testable assumptions.

An assumption is either something that we believe is true, or can be something that we’re expecting to happen. It will be based on what we think we know, as well as our personal experiences or individual viewpoints.

Assumptions will often turn out to be wrong, or at least partly wrong. This is okay as long as we’re clear about the assumptions we’re making.

In a discovery it’s especially important to have a shared understanding of the assumptions that you’re working with.

A discovery might start with a problem statement or an existing product or service. It might also start with a set of ideas, future vision, or a proposition intended to introduce a combination of new business and service models. All these areas of focus will mean that there are important assumptions to test.

A format for capturing assumptions at this point would be a combination of something like:

We think that [this] is true OR We think that [this] will happen Which is why we think [this] is happening OR Which we think creates [this] opportunity

If we’re then wanting to move from a list of assumptions to thinking about these statements as testable ‘hypotheses’, the key thing missing here is a unit of measurement.

The unit of measurement is user research

As this is about research and learning (discovery), the measure is simply what we want to learn from user research. Each assumption can become testable through a qualitative research plan.

How much research focus is required for each assumption depends on the level of certainty you need. This might be the certainty needed in order to start reframing a problem, or simply having enough confidence in a set of research insights and the design opportunities they start to create.

Working as part of a team, you will always need enough certainty to be in a position where you are confident enough to take the next steps to move forward.

Building on the previous format for capturing assumptions, we can easily add user research as a unit of measurement:

We think that [this] is true OR We think that [this] will happen Which is why we think [this] is happening OR Which we think creates [this] opportunity We can learn more about this assumption by speaking to [these] users in [these] scenarios

While I’ve focussed this example on qualitative research we could also gather quantitive data to learn more about each assumption. Quantitive data can help us to understand the size and scale of the impact and relative importance for each of a set of assumptions. It’s important to remember that some assumptions will always be more significant than others. Understanding their relative importance should be an additional focus of discovery work.

To summarise:

We are using research to reduce risk, or to increase the certainty we have in the assumptions we are making, as well as understanding their relative importance. 

Sometimes, research will show that there is no viability or service needed compared to an initial set of assumptions made from within a business, team or organisation. But, more often than not, research will help reframe a problem into a different set of opportunities–often dependent on testing and learning what does and doesn’t work using a design ( prototyping ) process.

Using assumptions as the basis of a research plan

A good approach for planning discovery research is to start with your assumptions. From here, you can plan for who you need to talk to, and focus time and effort in the right places–turning each assumption into a research hypothesis.

Make sure you capture any assumptions about where you believe you will find the people that you need to talk to. And also list the needs you believe these people have in relation to the problem or type of product/service/policy area that you’re working in.

A useful way to focus and make a start is by listing the following:

• These are the assumptions we have/are the most important assumptions we believe we are making.
• These are key questions we think we need to answer to learn about these assumptions. 
• These are the people we think we need to talk to about these assumptions, and where we think we can find them.
• These are the needs that we believe these people have in relation to our product/service/policy area.

Once you’ve listed all of these, it should be possible to create a full set of research hypotheses that you can use as an anchor for your work.

An anchor for learning

Discovery in my mind is less about about a fixed phase of research, and more about a mindset and approach to continuous learning.

The way I’ve framed this post works on the basis that discovery is always about starting with what we think we know already, or working from a shared set of assumptions and/or experiences.

When focused on discovery, I don’t think we should set aside all prior experience and knowledge. This includes our own knowledge, as well as knowledge built from a detailed understanding, and the direct experiences of people and specialist domains within the organisations we’re working with.

It’s more important to treat all previous experience and knowledge as a starting point. This means being aware that, taken as part of a new context, this prior knowledge and any set of assumptions may well prove to be wrong or less relevant to a new understanding of user needs, and a broader view of policy, organisations and future business models or services.

Starting with a clear and shared understanding of the assumptions you are making is a useful approach. Allowing you to move quickly to clearly defined research hypotheses, which can then act as an anchor point for discovery research and learning.

March 10th, 2019. Posted in - research

You can follow all Ben's posts by subscribing to the RSS feed or follow him on Twitter for more regular updates.

This is my blog where I’ve been writing for 18 years. You can follow all of my posts by subscribing to this RSS feed . You can also find me on Bluesky , less frequently now on X (formally Twitter) , and on LinkedIn .

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What is Research Hypothesis: Definition, Types, and How to Develop

Read the blog to learn how a research hypothesis provides a clear and focused direction for a study and helps formulate research questions.

June 28, 2024

In this Article

A research hypothesis provides a clear, testable statement that guides the direction and focus of a study.

The benefit is that the hypothesis makes selecting appropriate research methods or statistical means possible, making the analysis more effective and achieving a result. Above all, the idea selected for the research also makes the study more focused, and the hypothesis does that best of all. Finally, when researchers propose and test a hypothesis, they can confirm, enhance, reconsider, or reject any theories.

In this blog, we'll explore the concept of a research hypothesis, its significance in research, and the various types utilized in scientific studies. Additionally, we'll provide a step-by-step guide on formulating your research hypothesis and methods for testing and evaluating it.

What is a Research Hypothesis? 

A research hypothesis is a foundational element in both qualitative and quantitative research . It is a precise, testable statement that predicts a possible relationship between two or more variables. This hypothesis is developed based on existing theories, observations, or previous research and aims to provide a direction for further investigation.

A research hypothesis starts with a question a researcher is trying to answer. It implies its effect or outcome and provides a basic ground to construct investigations, surveys, or other methods. It explains what a researcher can expect to find. Once the expectations are clearly stated, a researcher will build the methodology by choosing methods and tools for data collection and analysis.

Examples of Research Hypothesis

Here are some examples of research hypotheses across various fields:

• Hypothesis: Individuals who practice mindfulness meditation daily will report lower levels of stress compared to those who do not practice mindfulness.
• Independent Variable: Mindfulness meditation practice.
• Dependent Variable: Levels of stress.
• Hypothesis: Students who receive personalized tutoring in math will perform better on standardized tests than those who do not.
• Independent Variable: Personalized tutoring in math.
• Dependent Variable: Performance on standardized tests.
• Hypothesis: Consumers exposed to advertisements with emotional appeals will have a higher purchase intention than those with rational appeals.
• Independent Variable: Type of advertisement appeal (emotional vs. rational).
• Dependent Variable: Purchase intent .
• Hypothesis: Increasing the minimum wage will decrease employee turnover rates in the retail sector.
• Independent Variable: Minimum wage increase.
• Dependent Variable: Employee turnover rates in the retail sector.

Technology:

• Hypothesis: Users who receive personalized recommendations on a streaming platform will spend more time watching content than users who do not receive personalized recommendations.
• Independent Variable: Personalized recommendations.
• Dependent Variable: Time spent watching content.

[ Note : Here, Independent Variable is the factor manipulated or controlled in an experiment to observe its effect.

Dependent Variable is the factor that is measured or observed in an experiment to assess the impact of the independent variable.]

What is the Importance of Hypothesis in Research?

The importance of a hypothesis in research cannot be overstated, as it serves several crucial functions in the scientific inquiry process. 

Here are the key reasons why hypotheses are fundamental to research:

1. Guides the Research Process

A hypothesis gives a study a clear direction as it outlines what you intend to study and establishes the relationship you are trying to find between variables. It is precise and to the point, which helps formulate your research questions and plan your methods. Using a hypothesis helps organize the testing process from the beginning to the end of the study.

2. Defines the Variables

A well-formulated hypothesis specifies the independent and dependent variables. It defines the object of manipulation and measurement. According to the definition, the hypothesis is an assumption about the relationship between the objects of study. Since statistics is a field of research, the hypothesis is a predictive statement that can be tested empirically.

3. Facilitates Testability and Empirical Investigation

A well-defined hypothesis indicates a clear relationship between the studied variables, thus providing a foundation for designing experiments and observations. In some cases, a null hypothesis is stated to subsequently apply the appropriate statistical test to either validate an already formulated and appropriate hypothesis or reject it.

4. Enhances Objectivity

A hypothesis helps minimize researcher bias by proposing a specific prediction. It forces the researcher to rely on empirical data rather than subjective opinions or beliefs. This objectivity is crucial for maintaining the integrity of the scientific process and ensuring that the findings are credible and reliable.

5. Promotes Critical Thinking and Theoretical Frameworks

Creating a reasonable and viable hypothesis starts with deeply understanding the problem and the field. With a clear sense of the scope of existing evidence and knowledge, there would be a way to go beyond what other researchers have already done. By thoroughly reviewing the literature, researchers are in a position to critically evaluate it and identify problems or questions that remain unresolved. 

6. Enables Structured Analysis and Interpretation

A hypothesis is a tentative assumption that provides a context for data analysis and interpretation. It allows for determining specific statistical tests to run and understanding how to interpret them. If the results support the hypothesis, then there is sufficient evidence to claim and infer that the chosen variables are related in a particular way to each other. 

If the hypothesis does not match the outcomes, it raises the question of the theoretical assumptions supporting it and additional testing that may be indicated.

7. Drives Scientific Progress

Testing hypotheses continually allows researchers to enrich knowledge beyond merely investigating a particular aspect. The data supporting both hypotheses, the data refuting them, may give rise to new theories, which may serve as the foundation for new research. Such a loop significantly benefits researchers who need to extend their understanding of a particular aspect of the outer world.

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What Are The Types of Research Hypotheses?

Research hypotheses can broadly be categorized into several types, each serving different purposes in scientific inquiry. 

Here are the main types of research hypotheses:

1. Simple Hypothesis

A simple hypothesis posits a relationship between two variables. It suggests a direct cause-and-effect relationship without specifying the direction of the effect. For example:

"Increased exercise leads to improved cardiovascular health."

2. Complex Hypothesis

Complex hypotheses involve relationships between multiple variables. These hypotheses may propose how several factors interact to produce a particular outcome. For example:

"The interaction between genetic predisposition, diet, and exercise influences longevity."

3. Associative Hypothesis

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

"There is a correlation between income level and access to healthcare services."

4. Causal Hypothesis

A causal hypothesis asserts that changes in one variable directly cause changes in another. It implies a cause-and-effect relationship that can be tested through experimentation or controlled observation. For example:

"Increased consumption of sugary drinks causes an increase in body weight."

5. Directional Hypothesis

A directional hypothesis predicts the direction of the relationship between variables. It specifies whether one variable will increase or decrease in response to changes in another variable. For example:

"Higher levels of education lead to higher income levels."

6. Non-directional Hypothesis

A non-directional hypothesis does not predict the direction of the relationship between variables. It simply suggests that there is a relationship without specifying whether one variable will increase or decrease in response to changes in another variable. For example:

"There is a relationship between social media use and levels of anxiety."

7. Null Hypothesis (H₀)

The null hypothesis states no significant relationship exists between the variables being studied. It proposes that any observed differences or effects are due to random chance or sampling error. It is often used to test against the alternative hypothesis (H₁), which proposes the existence of a relationship or effect. For example:

"There is no significant difference in test scores between students who study with music and students who study in silence."

How to Develop a Research Hypothesis?

Developing a research hypothesis involves a systematic process to ensure clarity, testability, and relevance to the research question. Here’s a step-by-step guide on how to develop a research hypothesis:

Step 1: Identify the Research Problem or Question

Start by clearly defining the research problem or question you want to investigate. This could be based on gaps in existing literature, observations, theories, or practical issues.

Step 2: Review Existing Literature

Conduct a thorough review of relevant literature to understand what is already known about the topic. Identify theories, findings, and gaps in knowledge that can help inform the development of your hypothesis.

Step 3: Specify Variables

Identify the variables involved in your study. Variables are measurable traits, conditions, or characteristics that can change or vary. 

Specifically, determine:

Independent Variable: The factor you manipulate or study in your research.

Dependent Variable: The outcome or response you are measuring or observing about the independent variable.

Step 4: Formulate a Hypothesis

Formulate a clear and specific hypothesis based on your research problem, literature review, and identified variables. A good hypothesis should:

State the expected relationship between the independent and dependent variables.

Be testable through empirical research methods (e.g., experiments, surveys, observations).

Be concise and specific, avoiding ambiguity.

Simple hypothesis: "Increased exposure to sunlight leads to higher levels of vitamin D in humans."

Directional hypothesis: "Children who participate in regular physical activity will have lower levels of obesity than children who do not."

Non-directional hypothesis: "There is a relationship between job satisfaction and employee turnover."

Step 5: Consider Alternative Hypotheses

While formulating your hypothesis, consider alternative explanations or hypotheses that could also explain the relationship between your variables. This helps in ensuring that your hypothesis is well-grounded and comprehensive.

Step 6: Ensure Testability

Ensure that your hypothesis is testable using appropriate research methods and techniques. Define how to measure or manipulate the variables to gather empirical evidence supporting or refuting your hypothesis.

Step 7: Write and Refine

Write down your hypothesis in a clear and concise statement. Revise and refine it as needed to improve clarity and specificity. Ensure that it aligns with the objectives of your study and effectively addresses the research question.

Step 8: Seek Feedback

Before finalizing your hypothesis, seek feedback from colleagues, mentors, or peers in your field. Their input can help identify potential weaknesses or ambiguities in your hypothesis and suggest improvements.

Step 9: Finalize Your Hypothesis

Once you have refined your hypothesis based on feedback and considerations, finalize it as the guiding statement for your research study.

Characteristics of a Good Research Hypothesis

A good research hypothesis possesses several key characteristics that make it effective and suitable for investigation:

1. Clear and Specific

The hypothesis should be precise in its wording and focus. It should clearly state what the researcher intends to investigate or test.

2. Testable

A hypothesis must be capable of being empirically tested and verified or falsified through observation or experimentation. This means there should be a way to gather data that supports or refutes the hypothesis.

3. Falsifiable

There must be a possibility of proving the hypothesis false. A hypothesis that cannot be proven false typically falls outside scientific inquiry. This criterion ensures that research remains objective and open to revision based on evidence.

4. Grounded in Theory

A good hypothesis is usually based on existing theories or literature. It should be informed by a solid understanding of the topic and build upon previous research findings or established principles.

5. Rationale

It should provide a logical rationale or explanation for the expected outcome. This rationale is often derived from the literature review or preliminary observations.

6. Empirical Relevance

The hypothesis should address a question relevant to the field of study and contribute to existing knowledge. It should propose a relationship or difference between variables that is worth investigating.

While the hypothesis should be clear and specific, it should also be concise and to the point. It typically consists of a statement or a few sentences summarizing the expected relationship between variables.

8. Variables

A hypothesis should identify the variables involved and specify how they are expected to relate. This includes independent variables (the factors that are manipulated or controlled) and dependent variables (the outcomes or effects being measured).

9. Observable and Measurable

The variables in the hypothesis should be observable and measurable, allowing for data collection that can be analyzed statistically.

10. Revisable

A hypothesis is not a conclusion but a tentative assumption or prediction that guides the research process. It should be open to revision based on the study's findings.

The Role of Decode in Testing Research Hypotheses

Decode is a powerful survey and consumer research platform powered by Insights AI, that can be instrumental in testing research hypotheses. 

Here's how Decode can support you in this process:

• Survey Design and Data Collection: Craft targeted questions using Decode's intuitive interface to gather relevant data for your research.
• Exploratory Research: Conduct exploratory research to understand the landscape of your topic—Leverage Decode's functionalities for surveys and feedback mechanisms to gain valuable insights from your target audience.
• Literature Review and Background Research: Supplement your literature review by collecting data on sample populations' opinions, experiences, and preferences through Decode surveys . This combined data and a thorough literature evaluation can help you build a well-grounded hypothesis with a strong foundation in real-world knowledge.
• Identifying Variables: Design targeted survey questions within Decode to pinpoint relevant variables crucial to your research topic.
• Testing Assumptions: Before solidifying your research hypothesis, informally test your assumptions using surveys created on Decode. This allows for early feedback and potential refinement.
• Data Analysis Tools: Decode provides built-in data analysis tools. Utilize these tools to uncover patterns, correlations, and trends within the data you collect through your surveys.
• Refining Your Hypotheses: As you gather data through Decode surveys, you can continuously adjust and refine your hypotheses based on the real-world responses you receive. This iterative process ensures your hypothesis stays aligned with the insights you uncover.

Final Words

A research hypothesis serves as a guide for scientists. It is a tested idea that applies across different fields, including medicine, social sciences, and natural sciences. Integrating theories with hands-on information assists researchers in exploring and discovering new information.

Decode is a valuable tool for researchers. It simplifies creating surveys, gathering data, and analyzing information. It supports all types of research, from forming hypotheses to testing them. Start a free trial to explore its features and maximize your research potential.

Frequently Asked Questions

What is a research hypothesis example.

A research hypothesis example is: "Students who receive daily math tutoring will have higher test scores than students who do not."

What do you write in a research hypothesis?

In a research hypothesis, you write a clear and testable statement predicting the relationship between two or more variables. It should specify the variables and the expected outcome.

What is the purpose of a research hypothesis?

A research hypothesis provides a focused direction for research. It guides the study design, data collection, and analysis by predicting a specific outcome that can be tested.

What are the three major types of hypotheses?

The three major types of hypotheses are:

• Null Hypothesis (H₀): States that there is no effect or relationship between variables.
• Alternative Hypothesis (H₁): Suggests that there is an effect or relationship between variables.
• Directional Hypothesis: Specifies the expected direction of the relationship between variables (e.g., positive or negative).

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Soham is a true Manchester United fan who finds joy in more than just football. Whether navigating the open road, scoring virtual goals in FIFA, reading novels, or enjoying quality time with friends, Soham embraces a life full of diverse passions.

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Problem Statements in UX Discovery

August 22, 2021 2021-08-22

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Running discoveries can be challenging. Many teams start discovery research with little direction as to what problem they want to solve. When this happens, discoveries meander and result in dwindling team and stakeholder morale. Worse still, some discoveries begin with investigating solutions, rather than the problems those solutions are intended to solve. (Remember: if you’re investigating only solutions in a discovery, you’re not doing a true discovery! )

To avoid these issues, spend time upfront to identify and frame the problem . If you don’t know the problem, you’re not going to have much luck solving it! The better a problem is articulated, the easier and more effectively it can be solved. One device that help teams to frame a problem is a problem statement.

In This Article:

What’s a problem statement, how to write a problem statement, problem statements don’t need to be negative, how to use problem statements.

Problem statement: A concise description of the problem that needs to be solved.

It’s a helpful scoping device, focusing the team on the problem it needs to explore and subsequently solve. A problem statement makes clear what needs to be done in discovery and what’s out of scope. Problem statements are also great communication tools; well-written ones can be used to gain buy-in from stakeholders on why it’s important to explore and solve the problem.

Here are some examples of problem statements.

• Users of our newspaper app often export content from our app, rather than sharing content through our app. This is a problem because target audiences are less likely to know that the content came from our app, leading to lower conversion rates. This is also a problem for app users, as exporting content is time-consuming and could lead to a decrease in app usage.
• Sales reps spend a long time planning which leads to visit each month. Because planning is done manually — using Excel spreadsheets and printed paper lists — sales reps find it difficult to meet their targets. Many have complained that keeping track of which leads to visit takes away from the time they can spend with them. This is a problem because, when targets are not met, the business risks losing revenue.
• Each year, many applicants call the contact center seeking an update on their application. Applicants often spend a long time waiting to speak to an agent. Because contact-center staff members lack access to case information, they are unable to answer queries from applicants. This situation causes frustration for both applicants and customer-contact staff and represents an avoidable cost to the department.

It's a good idea to write a problem statement as early as possible in your discovery, as it can help set discovery goals and objectives. Many teams will compose their problem statement in a discovery kick-off workshop.

A problem statement should include:

• The background of a problem. Which organization or department has the problem and what is the problem? Why has the problem arisen? Note that in some cases you may not know the exact causes of the problem. This is what discoveries are for: to uncover root causes. (In this case, you may add this aspect once you’ve done your research)
• The people affected by the problem. There could be multiple user groups affected by a specific problem in different ways. In the problem statement, you should call out how the problem affects users. In some cases, internal employees (particularly customer-support staff) can be affected by a problem, as they often bear the brunt of poor user experiences –- for example, by handling disgruntled customers.
• The impact of the problem on the organization. If the problem is not fixed, what will be the effect on the organization? Reputational damage? Paying unavoidable costs? Losing out-of-market share? In some cases, you may want to quantify the impact in order to convince your organization to fix the problem. Your discovery could involve working out how much this problem costs the organization, and this information could end up in your problem statement.

To gather the relevant facts for your problem statement, you can use a simple technique called the 5 Ws , which involves answering the questions below. This activity can be included in a discovery kick - off workshop with your team and stakeholders.

• Who is affected by the problem?
• What is the problem?
• Where does this problem occur?
• When does the problem occur?
• Why does the problem occur? Why is the problem important?

If you don’t have all the answers to the above, don’t panic! While you should know what the problem is, you may not know exactly why it came about. This is what your discovery should tackle. Throughout the discovery process, you can return to your problem statement and add to it.

It’s important that problem statements are written well to serve their purpose. A problem statement should :

• Not be a laundry list of unrelated problems . A discovery effort should have one problem statement, and the problem statement should be focused on one problem. Of course, a single problem could cause further problems, and those related problems can be added to your problem statement. But listing many unrelated problems is a sign that you’re tackling too much.
• Not contain a solution . Leave solutions out of your problem statement. At the beginning of discovery, there are too many unknowns, so the the best solution is not obvious. At the end of your discovery, you’ll be in a good position to confidently put forward solution ideas that address the problem and take into account what you’ve learned.
• Be brief . Problem statements are effective when they’re concise. If you can condense your problem statement down to a few sentences, others will quickly understand what you focus on and why, and what’s out of scope. Spend some time to draft and redraft the problem statement with your team.

The examples I’ve given so far are negative — talking about something that needs fixing. However, problem statements can also capture opportunities (in which case they are sometimes referred to as opportunity statements instead of problem statements, although they are written and used in the same way).

Here’s an example of a problem statement that highlights an opportunity, rather than a problem that needs to be fixed:

The process of purchasing a newly built home can take a long time and requires many offline activities. This means sales often take a long time to close. There’s an opportunity to make home buying quicker and easier, and thus improve customer-satisfaction ratings and sales.

In an opportunity statement, we need to highlight the gap between where we are now (the present state) and where we want to be in the future (the desired state). A good question to ask to highlight this gap is: What do we want to achieve?

Your problem statement can be used as the starting point for structuring your discovery work. For example, if the problem statement was about improving the home-buying process, the goal for the discovery should be to learn about opportunities to make home buying quicker and easier. Once we have a discovery goal, it becomes easier to know what unknowns need research. For example, in this case, we probably want to know things like:

• Which activities do homebuyers perceive as difficult or time-consuming?
• Which activities or use cases can slow down the home-buying process and why?
• What does the end-to-end journey currently look like?

As you begin discovery, you can return to your problem statement and refine it — particularly if you’ve learned root causes or how much a problem costs your organization. Another reason to update your problem statement is if the discovery changes direction — which can happen when new areas of interest are highlighted through exploratory research. Finally, at the end of the discovery process, the problem statement can be communicated alongside your findings and recommendations to provide the full narrative of the discovery process.

A problem statement is a concise description of the problem to be solved. Writing problem statements at the beginning of the discovery process can create alignment and buy-in around the problem to be solved and provide direction in subsequent discovery activities. To construct problem statements, focus on who the problem affects, how it does so, and why it’s important to solve the problem.

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How to write a research hypothesis

Last updated

19 January 2023

Reviewed by

Miroslav Damyanov

Start with a broad subject matter that excites you, so your curiosity will motivate your work. Conduct a literature search to determine the range of questions already addressed and spot any holes in the existing research.

Narrow the topics that interest you and determine your research question. Rather than focusing on a hole in the research, you might choose to challenge an existing assumption, a process called problematization. You may also find yourself with a short list of questions or related topics.

Use the FINER method to determine the single problem you'll address with your research. FINER stands for:

I nteresting

You need a feasible research question, meaning that there is a way to address the question. You should find it interesting, but so should a larger audience. Rather than repeating research that others have already conducted, your research hypothesis should test something novel or unique. 

The research must fall into accepted ethical parameters as defined by the government of your country and your university or college if you're an academic. You'll also need to come up with a relevant question since your research should provide a contribution to the existing research area.

This process typically narrows your shortlist down to a single problem you'd like to study and the variable you want to test. You're ready to write your hypothesis statements.

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• Types of research hypotheses

It is important to narrow your topic down to one idea before trying to write your research hypothesis. You'll only test one problem at a time. To do this, you'll write two hypotheses – a null hypothesis (H0) and an alternative hypothesis (Ha).

You'll come across many terms related to developing a research hypothesis or referring to a specific type of hypothesis. Let's take a quick look at these terms.

Null hypothesis

The term null hypothesis refers to a research hypothesis type that assumes no statistically significant relationship exists within a set of observations or data. It represents a claim that assumes that any observed relationship is due to chance. Represented as H0, the null represents the conjecture of the research.

Alternative hypothesis

The alternative hypothesis accompanies the null hypothesis. It states that the situation presented in the null hypothesis is false or untrue, and claims an observed effect in your test. This is typically denoted by Ha or H(n), where “n” stands for the number of alternative hypotheses. You can have more than one alternative hypothesis. 

Simple hypothesis

The term simple hypothesis refers to a hypothesis or theory that predicts the relationship between two variables - the independent (predictor) and the dependent (predicted). 

Complex hypothesis

The term complex hypothesis refers to a model – either quantitative (mathematical) or qualitative . A complex hypothesis states the surmised relationship between two or more potentially related variables.

Directional hypothesis

When creating a statistical hypothesis, the directional hypothesis (the null hypothesis) states an assumption regarding one parameter of a population. Some academics call this the “one-sided” hypothesis. The alternative hypothesis indicates whether the researcher tests for a positive or negative effect by including either the greater than (">") or less than ("<") sign.

Non-directional hypothesis

We refer to the alternative hypothesis in a statistical research question as a non-directional hypothesis. It includes the not equal ("≠") sign to show that the research tests whether or not an effect exists without specifying the effect's direction (positive or negative).

Associative hypothesis

The term associative hypothesis assumes a link between two variables but stops short of stating that one variable impacts the other. Academic statistical literature asserts in this sense that correlation does not imply causation. So, although the hypothesis notes the correlation between two variables – the independent and dependent - it does not predict how the two interact.

Logical hypothesis

Typically used in philosophy rather than science, researchers can't test a logical hypothesis because the technology or data set doesn't yet exist. A logical hypothesis uses logic as the basis of its assumptions. 

In some cases, a logical hypothesis can become an empirical hypothesis once technology provides an opportunity for testing. Until that time, the question remains too expensive or complex to address. Note that a logical hypothesis is not a statistical hypothesis.

Empirical hypothesis

When we consider the opposite of a logical hypothesis, we call this an empirical or working hypothesis. This type of hypothesis considers a scientifically measurable question. A researcher can consider and test an empirical hypothesis through replicable tests, observations, and measurements.

Statistical hypothesis

The term statistical hypothesis refers to a test of a theory that uses representative statistical models to test relationships between variables to draw conclusions regarding a large population. This requires an existing large data set, commonly referred to as big data, or implementing a survey to obtain original statistical information to form a data set for the study. 

Testing this type of hypothesis requires the use of random samples. Note that the null and alternative hypotheses are used in statistical hypothesis testing.

Causal hypothesis

The term causal hypothesis refers to a research hypothesis that tests a cause-and-effect relationship. A causal hypothesis is utilized when conducting experimental or quasi-experimental research.

Descriptive hypothesis

The term descriptive hypothesis refers to a research hypothesis used in non-experimental research, specifying an influence in the relationship between two variables.

• What makes an effective research hypothesis?

An effective research hypothesis offers a clearly defined, specific statement, using simple wording that contains no assumptions or generalizations, and that you can test. A well-written hypothesis should predict the tested relationship and its outcome. It contains zero ambiguity and offers results you can observe and test. 

The research hypothesis should address a question relevant to a research area. Overall, your research hypothesis needs the following essentials:

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

• How to develop a good research hypothesis

In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects:

the parameter you'll test

the test's direction (left-tailed, right-tailed, or non-directional)

the hypothesized parameter value

Any quantitative research includes a hypothesized parameter value of a mean, a proportion, or the difference between two proportions. Here's how to note each parameter:

Single mean (μ)

Paired means (μd)

Single proportion (p)

Difference between two independent means (μ1−μ2)

Difference between two proportions (p1−p2)

Simple linear regression slope (β)

Correlation (ρ)

Defining these parameters and determining whether you want to test the mean, proportion, or differences helps you determine the statistical tests you'll conduct to analyze your data. When writing your hypothesis, you only need to decide which parameter to test and in what overarching way.

The null research hypothesis must include everyday language, in a single sentence, stating the problem you want to solve. Write it as an if-then statement with defined variables. Write an alternative research hypothesis that states the opposite.

• What is the correct format for writing a hypothesis?

The following example shows the proper format and textual content of a hypothesis. It follows commonly accepted academic standards.

Null hypothesis (H0): High school students who participate in varsity sports as opposed to those who do not, fail to score higher on leadership tests than students who do not participate.

Alternative hypothesis (H1): High school students who play a varsity sport as opposed to those who do not participate in team athletics will score higher on leadership tests than students who do not participate in athletics.

The research question tests the correlation between varsity sports participation and leadership qualities expressed as a score on leadership tests. It compares the population of athletes to non-athletes.

• What are the five steps of a hypothesis?

Once you decide on the specific problem or question you want to address, you can write your research hypothesis. Use this five-step system to hone your null hypothesis and generate your alternative hypothesis.

Step 1 : Create your research question. This topic should interest and excite you; answering it provides relevant information to an industry or academic area.

Step 2 : Conduct a literature review to gather essential existing research.

Step 3 : Write a clear, strong, simply worded sentence that explains your test parameter, test direction, and hypothesized parameter.

Step 4 : Read it a few times. Have others read it and ask them what they think it means. Refine your statement accordingly until it becomes understandable to everyone. While not everyone can or will comprehend every research study conducted, any person from the general population should be able to read your hypothesis and alternative hypothesis and understand the essential question you want to answer.

Step 5 : Re-write your null hypothesis until it reads simply and understandably. Write your alternative hypothesis.

What is the Red Queen hypothesis?

Some hypotheses are well-known, such as the Red Queen hypothesis. Choose your wording carefully, since you could become like the famed scientist Dr. Leigh Van Valen. In 1973, Dr. Van Valen proposed the Red Queen hypothesis to describe coevolutionary activity, specifically reciprocal evolutionary effects between species to explain extinction rates in the fossil record. 

Essentially, Van Valen theorized that to survive, each species remains in a constant state of adaptation, evolution, and proliferation, and constantly competes for survival alongside other species doing the same. Only by doing this can a species avoid extinction. Van Valen took the hypothesis title from the Lewis Carroll book, "Through the Looking Glass," which contains a key character named the Red Queen who explains to Alice that for all of her running, she's merely running in place.

• Getting started with your research

In conclusion, once you write your null hypothesis (H0) and an alternative hypothesis (Ha), you’ve essentially authored the elevator pitch of your research. These two one-sentence statements describe your topic in simple, understandable terms that both professionals and laymen can understand. They provide the starting point of your research project.

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Creating A User Research Plan (with Examples)

UX research helps to test hypothesis you have about users prior to design. Sadly, not every UX design project starts with user research, and that’s because it takes a lot of time to recruit participants, run UX research projects, and sumamrize findings.

Good research, nevertheless, ensures that your product team doesn’t build the wrong functionality that would cost you valuable resources and make you vulnerable to losing customers.

In this article, you’ll see how you can use UX research plan to get stakeholder’s buy-in and create research reports that’s full of valuable advice for product design. Let’s go.

At the end, when you have your research complete, launch the right tool for your design process. For that, try UXPin, an end-to-end design tool for interactive prototyping that brings design and product development together.

Designers can create a powerful prototypes, show them to product managers who can interact with the design instead of just looking at it. Then, they give the design to engineers who can get all the specs and some code to kickstart front-end design with.

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What is a UX Research Plan?

A UX research plan helps to set expectations and document the essentials you need to communicate to stakeholders and clients. Your company needs a strong business case for every user research session, complete with research objectives, goals, methods, and logistical needs for the study.

UX Research Plan Elements

Every UX research plan should start with a solid outline. That’s where templates come in handy. They help you structure your UX research project in a way that team members and stakeholders see value in completing research process.

Master templates are the best way to create a successful and effective UX research plan. Using a template as a starting point makes planning and writing easier and helps you and your team stay focused on the who, what, why, and when of research. Read on for tips and examples for how you can build a user research plan that works.

UX Research Plan Background

The background section should offer your clients and stakeholders a few sentences on why you are creating a user research plan and what it will accomplish. It should orient readers to the needs and expectations behind the purpose of the study. It should also include a problem statement, which is the primary question you’re setting out to answer with your research findings. 

Example Background

The purpose of this study is to understand the major pain points users experience in using our website/app and how these contribute to issues such as cart abandonment, returned items, and low customer loyalty. 

We will be using usability testing to follow the user’s experience of our website/app and the obstacles they encounter leading up to the point of purchase. We will also be using generative research techniques to better understand the customer’s experience of our brand and the challenges and needs they face in making a purchase. 

UX Research Plan Objectives

Before getting into the nitty-gritty of your user research plan, you first want to focus on your research objectives. This step outlines the reasons you are conducting a UX research plan in the first place. Why are you carrying out this research? What are the end goals you have after completing all the work?

Seeking out answers to these questions should be a collaborative effort between you and your stakeholders. It’s also helpful to consider discussions and learnings from past clients and projects to create metrics for your UX research plan. 

Objectives and Success Metrics

Research objectives will be different for every project, but they should always be actionable and specific. 

Example Objectives

• Understand how users currently go about tracking orders on our website
• Understand what actions customers take when they consider buying a new [product we offer]
• Learn about competitor websites/apps customers are using to buy [product we offer]
• Evaluate pain points customers are experiencing in using our website/app

And here are some examples to help you determine the success of your UX research plan.

Example Success Metrics

• What information are we trying to collect about users?
• What scales/documents/statistics do we intend to create?
• What decisions will these materials help to make? 

UX Research Plan Methodology

This step should be a short and sweet description of the research methods you will use to answer the research objectives. It should include both secondary and primary methods. Generative methods, such as user interviews and open-ended questions, help uncover motivations or more general insights, while UX testing helps to evaluate the usability and experience of your product. 

Research Scope & Focus Areas

Clearly outlining the research scope and focus areas helps to facilitate efficient user research planning. The more you’re able to hone in on the specifics of what information you are wanting to collect, the less overwhelmed you will be in the process. It also helps avoid inundating your clients with unnecessary information. 

To keep research-focused, this section should include:

• 3-6 question topics (e.g. How do users spend their time on a website?)
• Design Focus Components, including interface qualities (e.g. Usability, Training, Efficiency, Satisfaction)
• Primary User Scenarios (e.g. Scenarios in which pain points are most problematic; scenarios you have the least information about, etc.)

Example Methodology

For this study, we’re conducting a 30-minute usability test to evaluate our user’s experience of our app/website. A secondary method will be to conduct one-on-one generative research interviews to better understand our customers and empathize with their needs. 

UX Research Plan Participant Profiles

Once you’ve defined objectives methodology and focus areas, it’s time to outline the participants you’ll need to get the required insights. Participant profiles help you determine who you want to recruit, or an approximation of your users, to optimize recruiting efforts. Here are a few examples of how to ensure you’ll get the best participants for your study. 

Define your target user by collaborating with internal stakeholders, marketing, sales, and customer support. With their help, you can create approximations about who your users are. This is a great starting point for finding the right participants for your study. 

Compare yourself to your competitors and create participant profiles based on their audiences. Recruiting people who use a competitor’s product can be an excellent way to glean insights into how to further improve your product. 

Outline a screening process. Participant profiles should include any relevant information concerning your target audience, including behaviors, needs, demographics, geography, etc. Including the right criteria will help you evaluate whether or not to include certain individuals in your user research plan. 

This Nielsen Norman article offers some great information about defining and recruiting the right participants for your study. 

UX Research Plan Timeline

This is optional, but many UX research plans include a timeline that offers clients and stakeholders a general overview of how long the research will take. It helps to set expectations for the final results as well as allowing you to create a schedule for research sessions, debriefing, follow-up, and deliverables. 

Timeline Example: 

Approximately 6-8 weeks for identifying objectives, creating participant profiles, recruitment, in-person meetings, qualitative research, and analysis. 

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15 Hypothesis Examples

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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A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021).  SAGE research methods foundations . SAGE Publications Ltd.

Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance.  Sleep medicine reviews ,  10 (5), 323-337.

Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression.  Psychiatry Research ,  309 , 114406.

King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease.  Molecular nutrition & food research ,  49 (6), 594-600.

Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.

Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.

Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

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What is a Hypothesis?

Table of Contents

Defining the hypothesis, the role of a hypothesis in the scientific method, types of hypotheses, hypothesis formulation, hypotheses and variables.

• The Importance of Testing Hypotheses
• The Hypothesis and Sociological Theory

In sociology, as in other scientific disciplines, the hypothesis serves as a crucial building block for research. It is a central element that directs the inquiry and provides a framework for testing the relationships between social phenomena. This article will explore what a hypothesis is, how it is formulated, and its role within the broader scientific method. By understanding the hypothesis, students of sociology can grasp how sociologists construct and test theories about the social world.

A hypothesis is a specific, testable statement about the relationship between two or more variables. It acts as a proposed explanation or prediction based on limited evidence, which researchers then test through empirical investigation. In essence, it is a statement that can be supported or refuted by data gathered from observation, experimentation, or other forms of systematic inquiry. The hypothesis typically takes the form of an “if-then” statement: if one variable changes, then another will change in response.

In sociological research, a hypothesis helps to focus the investigation by offering a clear proposition that can be tested. For instance, a sociologist might hypothesize that an increase in education levels leads to a decrease in crime rates. This hypothesis gives the researcher a direction, guiding them to collect data on education and crime, and analyze the relationship between the two variables. By doing so, the hypothesis serves as a tool for making sense of complex social phenomena.

The hypothesis is a key component of the scientific method, which is the systematic process by which sociologists and other scientists investigate the world. The scientific method begins with an observation of the world, followed by the formulation of a question or problem. Based on prior knowledge, theory, or preliminary observations, researchers then develop a hypothesis, which predicts an outcome or proposes a relationship between variables.

Once a hypothesis is established, researchers gather data to test it. If the data supports the hypothesis, it may be used to build a broader theory or to further refine the understanding of the social phenomenon in question. If the data contradicts the hypothesis, researchers may revise their hypothesis or abandon it altogether, depending on the strength of the evidence. In either case, the hypothesis helps to organize the research process, ensuring that it remains focused and methodologically sound.

In sociology, this method is particularly important because the social world is highly complex. Researchers must navigate a vast range of variables—age, gender, class, race, education, and countless others—that interact in unpredictable ways. A well-constructed hypothesis allows sociologists to narrow their focus to a manageable set of variables, making the investigation more precise and efficient.

Sociologists use different types of hypotheses, depending on the nature of their research question and the methods they plan to use. Broadly speaking, hypotheses can be classified into two main types: null hypotheses and alternative (or research) hypotheses.

Null Hypothesis

The null hypothesis, denoted as H0, states that there is no relationship between the variables being studied. It is a default assumption that any observed differences or relationships are due to random chance rather than a real underlying cause. In research, the null hypothesis serves as a point of comparison. Researchers collect data to see if the results allow them to reject the null hypothesis in favor of an alternative explanation.

For example, a sociologist studying the relationship between income and political participation might propose a null hypothesis that income has no effect on political participation. The goal of the research would then be to determine whether this null hypothesis can be rejected based on the data. If the data shows a significant correlation between income and political participation, the null hypothesis would be rejected.

Alternative Hypothesis

The alternative hypothesis, denoted as H1 or Ha, proposes that there is a significant relationship between the variables. This is the hypothesis that researchers aim to support with their data. In contrast to the null hypothesis, the alternative hypothesis predicts a specific direction or effect. For example, a researcher might hypothesize that higher levels of education lead to greater political engagement. In this case, the alternative hypothesis is proposing a positive correlation between the two variables.

The alternative hypothesis is the one that guides the research design, as it directs the researcher toward gathering evidence that will either support or refute the predicted relationship. The research process is structured around testing this hypothesis and determining whether the evidence is strong enough to reject the null hypothesis.

The process of formulating a hypothesis is both an art and a science. It requires a deep understanding of the social phenomena under investigation, as well as a clear sense of what is possible to observe and measure. Hypothesis formulation is closely linked to the theoretical framework that guides the research. Sociologists draw on existing theories to generate hypotheses, ensuring that their predictions are grounded in established knowledge.

To formulate a good hypothesis, a researcher must identify the key variables and determine how they are expected to relate to one another. Variables are the factors or characteristics that are being measured in a study. In sociology, these variables often include social attributes such as class, race, gender, age, education, and income, as well as behavioral variables like voting, criminal activity, or social participation.

For example, a sociologist studying the effects of social media on self-esteem might propose the following hypothesis: “Increased time spent on social media leads to lower levels of self-esteem among adolescents.” Here, the independent variable is the time spent on social media, and the dependent variable is the level of self-esteem. The hypothesis predicts a negative relationship between the two variables: as time spent on social media increases, self-esteem decreases.

A strong hypothesis has several key characteristics. It should be clear and specific, meaning that it unambiguously states the relationship between the variables. It should also be testable, meaning that it can be supported or refuted through empirical investigation. Finally, it should be grounded in theory, meaning that it is based on existing knowledge about the social phenomenon in question.

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