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How to write a hypothesis for marketing experimentation

hypothesis of marketing research

Creating your strongest marketing hypothesis

The potential for your marketing improvement depends on the strength of your testing hypotheses.

But where are you getting your test ideas from? Have you been scouring competitor sites, or perhaps pulling from previous designs on your site? The web is full of ideas and you’re full of ideas – there is no shortage of inspiration, that’s for sure.

Coming up with something you  want  to test isn’t hard to do. Coming up with something you  should  test can be hard to do.

Hard – yes. Impossible? No. Which is good news, because if you can’t create hypotheses for things that should be tested, your test results won’t mean mean much, and you probably shouldn’t be spending your time testing.

Taking the time to write your hypotheses correctly will help you structure your ideas, get better results, and avoid wasting traffic on poor test designs.

With this post, we’re getting advanced with marketing hypotheses, showing you how to write and structure your hypotheses to gain both business results and marketing insights!

By the time you finish reading, you’ll be able to:

  • Distinguish a solid hypothesis from a time-waster, and
  • Structure your solid hypothesis to get results  and  insights

To make this whole experience a bit more tangible, let’s track a sample idea from…well…idea to hypothesis.

Let’s say you identified a call-to-action (CTA)* while browsing the web, and you were inspired to test something similar on your own lead generation landing page. You think it might work for your users! Your idea is:

“My page needs a new CTA.”

*A call-to-action is the point where you, as a marketer, ask your prospect to do something on your page. It often includes a button or link to an action like “Buy”, “Sign up”, or “Request a quote”.

The basics: The correct marketing hypothesis format

A well-structured hypothesis provides insights whether it is proved, disproved, or results are inconclusive.

You should never phrase a marketing hypothesis as a question. It should be written as a statement that can be rejected or confirmed.

Further, it should be a statement geared toward revealing insights – with this in mind, it helps to imagine each statement followed by a  reason :

  • Changing _______ into ______ will increase [conversion goal], because:
  • Changing _______ into ______ will decrease [conversion goal], because:
  • Changing _______ into ______ will not affect [conversion goal], because:

Each of the above sentences ends with ‘because’ to set the expectation that there will be an explanation behind the results of whatever you’re testing.

It’s important to remember to plan ahead when you create a test, and think about explaining why the test turned out the way it did when the results come in.

Level up: Moving from a good to great hypothesis

Understanding what makes an idea worth testing is necessary for your optimization team.

If your tests are based on random ideas you googled or were suggested by a consultant, your testing process still has its training wheels on. Great hypotheses aren’t random. They’re based on rationale and aim for learning.

Hypotheses should be based on themes and analysis that show potential conversion barriers.

At Conversion, we call this investigation phase the “Explore Phase” where we use frameworks like the LIFT Model to understand the prospect’s unique perspective. (You can read more on the the full optimization process here).

A well-founded marketing hypothesis should also provide you with new, testable clues about your users regardless of whether or not the test wins, loses or yields inconclusive results.

These new insights should inform future testing: a solid hypothesis can help you quickly separate worthwhile ideas from the rest when planning follow-up tests.

“Ultimately, what matters most is that you have a hypothesis going into each experiment and you design each experiment to address that hypothesis.” – Nick So, VP of Delivery

Here’s a quick tip :

If you’re about to run a test that isn’t going to tell you anything new about your users and their motivations, it’s probably not worth investing your time in.

Let’s take this opportunity to refer back to your original idea:

Ok, but  what now ? To get actionable insights from ‘a new CTA’, you need to know why it behaved the way it did. You need to ask the right question.

To test the waters, maybe you changed the copy of the CTA button on your lead generation form from “Submit” to “Send demo request”. If this change leads to an increase in conversions, it could mean that your users require more clarity about what their information is being used for.

That’s a potential insight.

Based on this insight, you could follow up with another test that adds copy around the CTA about next steps: what the user should anticipate after they have submitted their information.

For example, will they be speaking to a specialist via email? Will something be waiting for them the next time they visit your site? You can test providing more information, and see if your users are interested in knowing it!

That’s the cool thing about a good hypothesis: the results of the test, while important (of course) aren’t the only component driving your future test ideas. The insights gleaned lead to further hypotheses and insights in a virtuous cycle.

It’s based on a science

The term “hypothesis” probably isn’t foreign to you. In fact, it may bring up memories of grade-school science class; it’s a critical part of the  scientific method .

The scientific method in testing follows a systematic routine that sets ideation up to predict the results of experiments via:

  • Collecting data and information through observation
  • Creating tentative descriptions of what is being observed
  • Forming  hypotheses  that predict different outcomes based on these observations
  • Testing your  hypotheses
  • Analyzing the data, drawing conclusions and insights from the results

Don’t worry! Hypothesizing may seem ‘sciency’, but it doesn’t have to be complicated in practice.

Hypothesizing simply helps ensure the results from your tests are quantifiable, and is necessary if you want to understand how the results reflect the change made in your test.

A strong marketing hypothesis allows testers to use a structured approach in order to discover what works, why it works, how it works, where it works, and who it works on.

“My page needs a new CTA.” Is this idea in its current state clear enough to help you understand what works? Maybe. Why it works? No. Where it works? Maybe. Who it works on? No.

Your idea needs refining.

Let’s pull back and take a broader look at the lead generation landing page we want to test.

Imagine the situation: you’ve been diligent in your data collection and you notice several recurrences of Clarity pain points – meaning that there are many unclear instances throughout the page’s messaging.

Rather than focusing on the CTA right off the bat, it may be more beneficial to deal with the bigger clarity issue.

Now you’re starting to think about solving your prospects conversion barriers rather than just testing random ideas!

If you believe the overall page is unclear, your overarching theme of inquiry might be positioned as:

  • “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

By testing a hypothesis that supports this clarity theme, you can gain confidence in the validity of it as an actionable marketing insight over time.

If the test results are negative : It may not be worth investigating this motivational barrier any further on this page. In this case, you could return to the data and look at the other motivational barriers that might be affecting user behavior.

If the test results are positive : You might want to continue to refine the clarity of the page’s message with further testing.

Typically, a test will start with a broad idea — you identify the changes to make, predict how those changes will impact your conversion goal, and write it out as a broad theme as shown above. Then, repeated tests aimed at that theme will confirm or undermine the strength of the underlying insight.

Building marketing hypotheses to create insights

You believe you’ve identified an overall problem on your landing page (there’s a problem with clarity). Now you want to understand how individual elements contribute to the problem, and the effect these individual elements have on your users.

It’s game time  – now you can start designing a hypothesis that will generate insights.

You believe your users need more clarity. You’re ready to dig deeper to find out if that’s true!

If a specific question needs answering, you should structure your test to make a single change. This isolation might ask: “What element are users most sensitive to when it comes to the lack of clarity?” and “What changes do I believe will support increasing clarity?”

At this point, you’ll want to boil down your overarching theme…

  • Improving the clarity of the page will reduce confusion and improve [conversion goal].

…into a quantifiable hypothesis that isolates key sections:

  • Changing the wording of this CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.

Does this answer what works? Yes: changing the wording on your CTA.

Does this answer why it works? Yes: reducing confusion about the next steps in the funnel.

Does this answer where it works? Yes: on this page, before the user enters this theoretical funnel.

Does this answer who it works on? No, this question demands another isolation. You might structure your hypothesis more like this:

  • Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion  for visitors coming from my email campaign  about the next steps in the funnel and improve order completions.

Now we’ve got a clear hypothesis. And one worth testing!

What makes a great hypothesis?

1. It’s testable.

2. It addresses conversion barriers.

3. It aims at gaining marketing insights.

Let’s compare:

The original idea : “My page needs a new CTA.”

Following the hypothesis structure : “A new CTA on my page will increase [conversion goal]”

The first test implied a problem with clarity, provides a potential theme : “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

The potential clarity theme leads to a new hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.”

Final refined hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions.”

Which test would you rather your team invest in?

Before you start your next test, take the time to do a proper analysis of the page you want to focus on. Do preliminary testing to define bigger issues, and use that information to refine and pinpoint your marketing hypothesis to give you forward-looking insights.

Doing this will help you avoid time-wasting tests, and enable you to start getting some insights for your team to keep testing!

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Hypotheses in Marketing Science: Literature Review and Publication Audit

  • Published: May 2001
  • Volume 12 , pages 171–187, ( 2001 )

Cite this article

  • J. Scott Armstrong 1 ,
  • Roderick J. Brodie 2 &
  • Andrew G. Parsons 2  

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We examined three approaches to research in marketing: exploratory hypotheses, dominant hypothesis, and competing hypotheses. Our review of empirical studies on scientific methodology suggests that the use of a single dominant hypothesis lacks objectivity relative to the use of exploratory and competing hypotheses approaches. We then conducted a publication audit of over 1,700 empirical papers in six leading marketing journals during 1984–1999. Of these, 74% used the dominant hypothesis approach, while 13% used multiple competing hypotheses, and 13% were exploratory. Competing hypotheses were more commonly used for studying methods (25%) than models (17%) and phenomena (7%). Changes in the approach to hypotheses since 1984 have been modest; there was a slight decrease in the percentage of competing hypotheses to 11%, which is explained primarily by an increasing proportion of papers on phenomena. Of the studies based on hypothesis testing, only 11% described the conditions under which the hypotheses would apply, and dominant hypotheses were below competing hypotheses in this regard. Marketing scientists differed substantially in their opinions about what types of studies should be published and what was published. On average, they did not think dominant hypotheses should be used as often as they were, and they underestimated their use.

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  • 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 .

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 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


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

Research bias

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

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hypothesis of marketing research

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 of marketing research

Expert Advice on Developing a Hypothesis for Marketing Experimentation 

  • Conversion Rate Optimization

Simbar Dube

Simbar Dube

Every marketing experimentation process has to have a solid hypothesis. 

That’s a must – unless you want to be roaming in the dark and heading towards a dead-end in your experimentation program.

Hypothesizing is the second phase of our SHIP optimization process here at Invesp.

hypothesis of marketing research

It comes after we have completed the research phase. 

This is an indication that we don’t just pull a hypothesis out of thin air. We always make sure that it is based on research data. 

But having a research-backed hypothesis doesn’t mean that the hypothesis will always be correct. In fact, tons of hypotheses bear inconclusive results or get disproved. 

The main idea of having a hypothesis in marketing experimentation is to help you gain insights – regardless of the testing outcome. 

By the time you finish reading this article, you’ll know: 

  • The essential tips on what to do when crafting a hypothesis for marketing experiments
  • How a marketing experiment hypothesis works 

How experts develop a solid hypothesis

The basics: marketing experimentation hypothesis.

A hypothesis is a research-based statement that aims to explain an observed trend and create a solution that will improve the result. This statement is an educated, testable prediction about what will happen.

It has to be stated in declarative form and not as a question.

“ If we add magnification info, product video and making virtual mirror buttons, will that improve engagement? ” is not declarative, but “ Improving the experience of product pages by adding magnification info, product video and making virtual mirror buttons will increase engagement ” is.

Here’s a quick example of how a hypothesis should be phrased: 

  • Replacing ___ with __ will increase [conversion goal] by [%], because:
  • Removing ___ and __ will decrease [conversion goal] by [%], because:
  • Changing ___ into __ will not affect [conversion goal], because:
  • Improving  ___ by  ___will increase [conversion goal], because: 

As you can see from the above sentences, a good hypothesis is written in clear and simple language. Reading your hypothesis should tell your team members exactly what you thought was going to happen in an experiment.

Another important element of a good hypothesis is that it defines the variables in easy-to-measure terms, like who the participants are, what changes during the testing, and what the effect of the changes will be: 

Example : Let’s say this is our hypothesis: 

Displaying full look items on every “continue shopping & view your bag” pop-up and highlighting the value of having a full look will improve the visibility of a full look, encourage visitors to add multiple items from the same look and that will increase the average order value, quantity with cross-selling by 3% .

Who are the participants : 


What changes during the testing : 

Displaying full look items on every “continue shopping & view your bag” pop-up and highlighting the value of having a full look…

What the effect of the changes will be:  

Will improve the visibility of a full look, encourage visitors to add multiple items from the same look and that will increase the average order value, quantity with cross-selling by 3% .

Don’t bite off more than you can chew! Answering some scientific questions can involve more than one experiment, each with its own hypothesis. so, you have to make sure your hypothesis is a specific statement relating to a single experiment.

How a Marketing Experimentation Hypothesis Works

Assuming that you have done conversion research and you have identified a list of issues ( UX or conversion-related problems) and potential revenue opportunities on the site. The next thing you’d want to do is to prioritize the issues and determine which issues will most impact the bottom line.

Having ranked the issues you need to test them to determine which solution works best. At this point, you don’t have a clear solution for the problems identified. So, to get better results and avoid wasting traffic on poor test designs, you need to make sure that your testing plan is guided. 

This is where a hypothesis comes into play. 

For each and every problem you’re aiming to address, you need to craft a hypothesis for it – unless the problem is a technical issue that can be solved right away without the need to hypothesize or test. 

One important thing you should note about an experimentation hypothesis is that it can be implemented in different ways.  

hypothesis of marketing research

This means that one hypothesis can have four or five different tests as illustrated in the image above. Khalid Saleh , the Invesp CEO, explains: 

“There are several ways that can be used to support one single hypothesis. Each and every way is a possible test scenario. And that means you also have to prioritize the test design you want to start with. Ultimately the name of the game is you want to find the idea that has the biggest possible impact on the bottom line with the least amount of effort. We use almost 18 different metrics to score all of those.”

In one of the recent tests we launched after watching video recordings, viewing heatmaps, and conducting expert reviews, we noticed that:  

  • Visitors were scrolling to the bottom of the page to fill out a calculator so as to get a free diet plan. 
  • Brand is missing 
  • Too many free diet plans – and this made it hard for visitors to choose and understand.  
  • No value proposition on the page
  • The copy didn’t mention the benefits of the paid program
  • There was no clear CTA for the next action

To help you understand, let’s have a look at how the original page looked like before we worked on it: 

hypothesis of marketing research

So our aim was to make the shopping experience seamless for visitors, make the page more appealing and not confusing. In order to do that, here is how we phrased the hypothesis for the page above: 

Improving the experience of optin landing pages by making the free offer accessible above the fold and highlighting the next action with a clear CTA and will increase the engagement on the offer and increase the conversion rate by 1%.

For this particular hypothesis, we had two design variations aligned to it:

hypothesis of marketing research

The two above designs are different, but they are aligned to one hypothesis. This goes on to show how one hypothesis can be implemented in different ways. Looking at the two variations above – which one do you think won?

Yes, you’re right, V2 was the winner. 

Considering that there are many ways you can implement one hypothesis, so when you launch a test and it fails, it doesn’t necessarily mean that the hypothesis was wrong. Khalid adds:

“A single failure of a test doesn’t mean that the hypothesis is incorrect. Nine times out of ten it’s because of the way you’ve implemented the hypothesis. Look at the way you’ve coded and look at the copy you’ve used – you are more likely going to find something wrong with it. Always be open.” 

So there are three things you should keep in mind when it comes to marketing experimentation hypotheses: 

  • It takes a while for this hypothesis to really fully test it.
  • A single failure doesn’t necessarily mean that the hypothesis is incorrect.
  • Whether a hypothesis is proved or disproved, you can still learn something about your users.

I know it’s never easy to develop a hypothesis that informs future testing – I mean it takes a lot of intense research behind the scenes, and tons of ideas to begin with. So, I reached out to six CRO experts for tips and advice to help you understand more about developing a solid hypothesis and what to include in it. 

Maurice   says that a solid hypothesis should have not more than one goal: 

Maurice Beerthuyzen – CRO/CXO Lead at ClickValue “Creating a hypothesis doesn’t begin at the hypothesis itself. It starts with research. What do you notice in your data, customer surveys, and other sources? Do you understand what happens on your website? When you notice an opportunity it is tempting to base one single A/B test on one hypothesis. Create hypothesis A and run a single test, and then move forward to the next test. With another hypothesis. But it is very rare that you solve your problem with only one hypothesis. Often a test provides several other questions. Questions which you can solve with running other tests. But based on that same hypothesis! We should not come up with a new hypothesis for every test. Another mistake that often happens is that we fill the hypothesis with multiple goals. Then we expect that the hypothesis will work on conversion rate, average order value, and/or Click Through Ratio. Of course, this is possible, but when you run your test, your hypothesis can only have one goal at once. And what if you have two goals? Just split the hypothesis then create a secondary hypothesis for your second goal. Every test has one primary goal. What if you find a winner on your secondary hypothesis? Rerun the test with the second hypothesis as the primary one.”

Jon believes that a strong hypothesis is built upon three pillars:

Jon MacDonald – President and Founder of The Good Respond to an established challenge – The challenge must have a strong background based on data, and the background should state an established challenge that the test is looking to address. Example: “Sign up form lacks proof of value, incorrectly assuming if users are on the page, they already want the product.” Propose a specific solution – What is the one, the single thing that is believed will address the stated challenge? Example: “Adding an image of the dashboard as a background to the signup form…”. State the assumed impact – The assumed impact should reference one specific, measurable optimization goal that was established prior to forming a hypothesis. Example: “…will increase signups.” So, if your hypothesis doesn’t have a specific, measurable goal like “will increase signups,” you’re not really stating a test hypothesis!”

Matt uses his own hypothesis builder to collate important data points into a single hypothesis. 

Matt Beischel – Founder of Corvus CRO Like Jon, Matt also breaks down his hypothesis writing process into three sections. Unlike Jon, Matt sections are: Comprehension Response Outcome I set it up so that the names neatly match the “CRO.” It’s a sort of “mad-libs” style fill-in-the-blank where each input is an important piece of information for building out a robust hypothesis. I consider these the minimum required data points for a good hypothesis; if you can’t completely fill out the form, then you don’t have a good hypothesis. Here’s a breakdown of each data point: Comprehension – Identifying something that can be improved upon Problem: “What is a problem we have?” Observation Method: “How did we identify the problem?” Response – Change that can cause improvement Variation: “What change do we think could solve the problem?” Location: “Where should the change occur?” Scope: “What are the conditions for the change?” Audience: “Who should the change affect?” Outcome – Measurable result of the change that determines the success Behavior Change : “What change in behavior are we trying to affect?” Primary KPI: “What is the important metric that determines business impact?” Secondary KPIs: “Other metrics that will help reinforce/refute the Primary KPI” Something else to consider is that I have a “user first” approach to formulating hypotheses. My process above is always considered within the context of how it would first benefit the user. Now, I do feel that a successful experiment should satisfy the needs of BOTH users and businesses, but always be in favor of the user. Notice that “Behavior Change” is the first thing listed in Outcome, not primary business KPI. Sure, at the end of the day you are working for the business’s best interests (both strategically and financially), but placing the user first will better inform your decision making and prioritization; there’s a reason that things like personas, user stories, surveys, session replays, reviews, etc. exist after all. A business-first ideology is how you end up with dark patterns and damaging brand credibility.”

One of the many mistakes that CROs make when writing a hypothesis is that they are focused on wins and not on insights. Shiva advises against this mindset:

Shiva Manjunath – Marketing Manager and CRO at Gartner “Test to learn, not test to win. It’s a very simple reframe of hypotheses but can have a magnitude of difference. Here’s an example: Test to Win Hypothesis: If I put a product video in the middle of the product page, I will improve add to cart rates and improve CVR. Test to Learn Hypothesis: If I put a product video on the product page, there will be high engagement with the video and it will positively influence traffic What you’re doing is framing your hypothesis, and test, in a particular way to learn as much as you can. That is where you gain marketing insights. The more you run ‘marketing insight’ tests, the more you will win. Why? The more you compound marketing insight learnings, your win velocity will start to increase as a proxy of the learnings you’ve achieved. Then, you’ll have a higher chance of winning in your tests – and the more you’ll be able to drive business results.”

Lorenzo  says it’s okay to focus on achieving a certain result as long as you are also getting an answer to: “Why is this event happening or not happening?”

Lorenzo Carreri – CRO Consultant “When I come up with a hypothesis for a new or iterative experiment, I always try to find an answer to a question. It could be something related to a problem people have or an opportunity to achieve a result or a way to learn something. The main question I want to answer is “Why is this event happening or not happening?” The question is driven by data, both qualitative and quantitative. The structure I use for stating my hypothesis is: From [data source], I noticed [this problem/opportunity] among [this audience of users] on [this page or multiple pages]. So I believe that by [offering this experiment solution], [this KPI] will [increase/decrease/stay the same].

Jakub Linowski says that hypotheses are meant to hold researchers accountable:

Jakub Linowski – Chief Editor of GoodUI “They do this by making your change and prediction more explicit. A typical hypothesis may be expressed as: If we change (X), then it will have some measurable effect (A). Unfortunately, this oversimplified format can also become a heavy burden to your experiment design with its extreme reductionism. However you decide to format your hypotheses, here are three suggestions for more flexibility to avoid limiting yourself. One Or More Changes To break out of the first limitation, we have to admit that our experiments may contain a single or multiple changes. Whereas the classic hypothesis encourages a single change or isolated variable, it’s not the only way we can run experiments. In the real world, it’s quite normal to see multiple design changes inside a single variation. One valid reason for doing this is when wishing to optimize a section of a website while aiming for a greater effect. As more positive changes compound together, there are times when teams decide to run bigger experiments. An experiment design (along with your hypotheses) therefore should allow for both single or multiple changes. One Or More Metrics A second limitation of many hypotheses is that they often ask us to only make a single prediction at a time. There are times when we might like to make multiple guesses or predictions to a set of metrics. A simple example of this might be a trade-off experiment with a guess of increased sales but decreased trial signups. Being able to express single or multiple metrics in our experimental designs should therefore be possible. Estimates, Directional Predictions, Or Unknowns Finally, traditional hypotheses also tend to force very simple directional predictions by asking us to guess whether something will increase or decrease. In reality, however, the fidelity of predictions can be higher or lower. On one hand, I’ve seen and made experiment estimations that contain specific numbers from prior data (ex: increase sales by 14%). While at other times it should also be acceptable to admit the unknown and leave the prediction blank. One example of this is when we are testing a completely novel idea without any prior data in a highly exploratory type of experiment. In such cases, it might be dishonest to make any sort of predictions and we should allow ourselves to express the unknown comfortably.”


So there you have it! Before you jump on launching a test, start by making sure that your hypothesis is solid and backed by research. Ask yourself the questions below when crafting a hypothesis for marketing experimentation:

  • Is the hypothesis backed by research?
  • Can the hypothesis be tested?
  • Does the hypothesis provide insights?
  • Does the hypothesis set the expectation that there will be an explanation behind the results of whatever you’re testing?

Don’t worry! Hypothesizing may seem like a very complicated process, but it’s not complicated in practice especially when you have done proper research.

If you enjoyed reading this article and you’d love to get the best CRO content – delivered by the best experts in the industry – straight to your inbox, every week. Please subscribe here .

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The Craft of Writing a Strong Hypothesis

Deeptanshu D

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.

hypothesis of marketing research

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Marketing research and theory

Posted on November 12, 2020 by Introspective-Mode in Comment Pieces , Food for Thought , Marketing Theory

I was inspired by the article (or open letter) written by Terry H. Grapentine and R. Kenneth Teas entitled “ From Information to Theory: it’s time for a new definition of marketing research ” which appears on the AMA’s  website,   (accessed October 2012) . 

The number and variety of theories which can guide us to better research designs, questionnaire construction, analysis, and interpretation of findings are almost infinite. In a follow-up post, I will explore a few more marketing theories. We have to start by recognising the inclusion of theory and creation of knowledge in the definition of marketing research. Next step is to let theory guide us in our every day’s work!


Market Research

9 Key stages in your marketing research process

You can conduct your own marketing research. Follow these steps, add your own flair, knowledge and creativity, and you’ll have bespoke research to be proud of.

Marketing research is the term used to cover the concept, development, placement and evolution of your product or service, its growing customer base and its branding – starting with brand awareness , and progressing to (everyone hopes) brand equity . Like any research, it needs a robust process to be credible and useful.

Marketing research uses four essential key factors known as the ‘marketing mix’ , or the Four Ps of Marketing :

  • Product (goods or service)
  • Price ( how much the customer pays )
  • Place (where the product is marketed)
  • Promotion (such as advertising and PR)

These four factors need to work in harmony for a product or service to be successful in its marketplace.

The marketing research process – an overview

A typical marketing research process is as follows:

  • Identify an issue, discuss alternatives and set out research objectives
  • Develop a research program
  • Choose a sample
  • Gather information
  • Gather data
  • Organize and analyze information and data
  • Present findings
  • Make research-based decisions
  • Take action based on insights

Step 1: Defining the marketing research problem

Defining a problem is the first step in the research process. In many ways, research starts with a problem facing management. This problem needs to be understood, the cause diagnosed, and solutions developed.

However, most management problems are not always easy to research, so they must first be translated into research problems. Once you approach the problem from a research angle, you can find a solution. For example, “sales are not growing” is a management problem, but translated into a research problem, it becomes “ why are sales not growing?” We can look at the expectations and experiences of several groups : potential customers, first-time buyers, and repeat purchasers. We can question whether the lack of sales is due to:

  • Poor expectations that lead to a general lack of desire to buy, or
  • Poor performance experience and a lack of desire to repurchase.

This, then, is the difference between a management problem and a research problem. Solving management problems focuses on actions: Do we advertise more? Do we change our advertising message? Do we change an under-performing product configuration? And if so, how?

Defining research problems, on the other hand, focus on the whys and hows, providing the insights you need to solve your management problem.

Step 2: Developing a research program: method of inquiry

The scientific method is the standard for investigation. It provides an opportunity for you to use existing knowledge as a starting point, and proceed impartially.

The scientific method includes the following steps:

  • Define a problem
  • Develop a hypothesis
  • Make predictions based on the hypothesis
  • Devise a test of the hypothesis
  • Conduct the test
  • Analyze the results

This terminology is similar to the stages in the research process. However, there are subtle differences in the way the steps are performed:

  • the scientific research method is objective and fact-based, using quantitative research and impartial analysis
  • the marketing research process can be subjective, using opinion and qualitative research, as well as personal judgment as you collect and analyze data

Step 3: Developing a research program: research method

As well as selecting a method of inquiry (objective or subjective), you must select a research method . There are two primary methodologies that can be used to answer any research question:

  • Experimental research : gives you the advantage of controlling extraneous variables and manipulating one or more variables that influence the process being implemented.
  • Non-experimental research : allows observation but not intervention – all you do is observe and report on your findings.

Step 4: Developing a research program: research design

Research design is a plan or framework for conducting marketing research and collecting data. It is defined as the specific methods and procedures you use to get the information you need.

There are three core types of marketing research designs: exploratory, descriptive, and causal . A thorough marketing research process incorporates elements of all of them.

Exploratory marketing research

This is a starting point for research. It’s used to reveal facts and opinions about a particular topic, and gain insight into the main points of an issue. Exploratory research is too much of a blunt instrument to base conclusive business decisions on, but it gives the foundation for more targeted study. You can use secondary research materials such as trade publications, books, journals and magazines and primary research using qualitative metrics, that can include open text surveys, interviews and focus groups.

Descriptive marketing research

This helps define the business problem or issue so that companies can make decisions, take action and monitor progress. Descriptive research is naturally quantitative – it needs to be measured and analyzed statistically , using more targeted surveys and questionnaires. You can use it to capture demographic information , evaluate a product or service for market, and monitor a target audience’s opinion and behaviors. Insights from descriptive research can inform conclusions about the market landscape and the product’s place in it.

Causal marketing research

This is useful to explore the cause and effect relationship between two or more variables. Like descriptive research , it uses quantitative methods, but it doesn’t merely report findings; it uses experiments to predict and test theories about a product or market. For example, researchers may change product packaging design or material, and measure what happens to sales as a result.

Step 5: Choose your sample

Your marketing research project will rarely examine an entire population. It’s more practical to use a sample - a smaller but accurate representation of the greater population. To design your sample, you’ll need to answer these questions:

  • Which base population is the sample to be selected from? Once you’ve established who your relevant population is (your research design process will have revealed this), you have a base for your sample. This will allow you to make inferences about a larger population.
  • What is the method (process) for sample selection? There are two methods of selecting a sample from a population:

1. Probability sampling : This relies on a random sampling of everyone within the larger population.

2. Non-probability sampling : This is based in part on the investigator’s judgment, and often uses convenience samples, or by other sampling methods that do not rely on probability.

  • What is your sample size? This important step involves cost and accuracy decisions. Larger samples generally reduce sampling error and increase accuracy, but also increase costs. Find out your perfect sample size with our calculator .

Step 6: Gather data

Your research design will develop as you select techniques to use. There are many channels for collecting data, and it’s helpful to differentiate it into O-data (Operational) and X-data (Experience):

  • O-data is your business’s hard numbers like costs, accounting, and sales. It tells you what has happened, but not why.
  • X-data gives you insights into the thoughts and emotions of the people involved: employees, customers, brand advocates.

When you combine O-data with X-data, you’ll be able to build a more complete picture about success and failure - you’ll know why. Maybe you’ve seen a drop in sales (O-data) for a particular product. Maybe customer service was lacking, the product was out of stock, or advertisements weren’t impactful or different enough: X-data will reveal the reason why those sales dropped. So, while differentiating these two data sets is important, when they are combined, and work with each other, the insights become powerful.

With mobile technology, it has become easier than ever to collect data. Survey research has come a long way since market researchers conducted face-to-face, postal, or telephone surveys. You can run research through:

  • Social media ( polls and listening )

Another way to collect data is by observation. Observing a customer’s or company’s past or present behavior can predict future purchasing decisions. Data collection techniques for predicting past behavior can include market segmentation , customer journey mapping and brand tracking .

Regardless of how you collect data, the process introduces another essential element to your research project: the importance of clear and constant communication .

And of course, to analyze information from survey or observation techniques, you must record your results . Gone are the days of spreadsheets. Feedback from surveys and listening channels can automatically feed into AI-powered analytics engines and produce results, in real-time, on dashboards.

Step 7: Analysis and interpretation

The words ‘ statistical analysis methods ’ aren’t usually guaranteed to set a room alight with excitement, but when you understand what they can do, the problems they can solve and the insights they can uncover, they seem a whole lot more compelling.

Statistical tests and data processing tools can reveal:

  • Whether data trends you see are meaningful or are just chance results
  • Your results in the context of other information you have
  • Whether one thing affecting your business is more significant than others
  • What your next research area should be
  • Insights that lead to meaningful changes

There are several types of statistical analysis tools used for surveys. You should make sure that the ones you choose:

  • Work on any platform - mobile, desktop, tablet etc.
  • Integrate with your existing systems
  • Are easy to use with user-friendly interfaces, straightforward menus, and automated data analysis
  • Incorporate statistical analysis so you don’t just process and present your data, but refine it, and generate insights and predictions.

Here are some of the most common tools:

  • Benchmarking : a way of taking outside factors into account so that you can adjust the parameters of your research. It ‘levels the playing field’ – so that your data and results are more meaningful in context. And gives you a more precise understanding of what’s happening.
  • Regression analysis : this is used for working out the relationship between two (or more) variables. It is useful for identifying the precise impact of a change in an independent variable.
  • T-test is used for comparing two data groups which have different mean values. For example, do women and men have different mean heights?
  • Analysis of variance (ANOVA) Similar to the T-test, ANOVA is a way of testing the differences between three or more independent groups to see if they’re statistically significant.
  • Cluster analysis : This organizes items into groups, or clusters, based on how closely associated they are.
  • Factor analysis: This is a way of condensing many variables into just a few, so that your research data is less unwieldy to work with.
  • Conjoint analysis : this will help you understand and predict why people make the choices they do. It asks people to make trade-offs when making decisions, just as they do in the real world, then analyzes the results to give the most popular outcome.
  • Crosstab analysis : this is a quantitative market research tool used to analyze ‘categorical data’ - variables that are different and mutually exclusive, such as: ‘men’ and ‘women’, or ‘under 30’ and ‘over 30’.
  • Text analysis and sentiment analysis : Analyzing human language and emotions is a rapidly-developing form of data processing, assigning positive, negative or neutral sentiment to customer messages and feedback.

Stats IQ can perform the most complicated statistical tests at the touch of a button using our online survey software , or data from other sources. Learn more about Stats iQ now .

Step 8: The marketing research results

Your marketing research process culminates in the research results. These should provide all the information the stakeholders and decision-makers need to understand the project.

The results will include:

  • all your information
  • a description of your research process
  • the results
  • conclusions
  • recommended courses of action

They should also be presented in a form, language and graphics that are easy to understand, with a balance between completeness and conciseness, neither leaving important information out or allowing it to get so technical that it overwhelms the readers.

Traditionally, you would prepare two written reports:

  • a technical report , discussing the methods, underlying assumptions and the detailed findings of the research project
  • a summary report , that summarizes the research process and presents the findings and conclusions simply.

There are now more engaging ways to present your findings than the traditional PowerPoint presentations, graphs, and face-to-face reports:

  • Live, interactive dashboards for sharing the most important information, as well as tracking a project in real time.
  • Results-reports visualizations – tables or graphs with data visuals on a shareable slide deck
  • Online presentation technology, such as Prezi
  • Visual storytelling with infographics
  • A single-page executive summary with key insights
  • A single-page stat sheet with the top-line stats

You can also make these results shareable so that decision-makers have all the information at their fingertips.

Step 9 Turn your insights into action

Insights are one thing, but they’re worth very little unless they inform immediate, positive action. Here are a few examples of how you can do this:

  • Stop customers leaving – negative sentiment among VIP customers gets picked up; the customer service team contacts the customers, resolves their issues, and avoids churn .
  • Act on important employee concerns – you can set certain topics, such as safety, or diversity and inclusion to trigger an automated notification or Slack message to HR. They can rapidly act to rectify the issue.
  • Address product issues – maybe deliveries are late, maybe too many products are faulty. When product feedback gets picked up through Smart Conversations, messages can be triggered to the delivery or product teams to jump on the problems immediately.
  • Improve your marketing effectiveness - Understand how your marketing is being received by potential customers, so you can find ways to better meet their needs
  • Grow your brand - Understand exactly what consumers are looking for, so you can make sure that you’re meeting their expectations

Download now: 8 Innovations to Modernize Market Research

Scott Smith

Scott Smith, Ph.D. is a contributor to the Qualtrics blog.

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

hypothesis of marketing research

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

A good research hypothesis needs to be very clear about what’s being assessed and very specific about the expected outcome.

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

A good research hypothesis must be testable. In other words, you must able to collect observable data in a scientifically rigorous fashion to test it.

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

hypothesis of marketing research

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

Research limitations vs delimitations


Lynnet Chikwaikwai

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

Dr. WuodArek

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


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

GANDI Benjamin

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

Lucile Dossou-Yovo

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

Egya Salihu

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

Mulugeta Tefera

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

Derek Jansen

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


could you please elaborate it more

Patricia Nyawir

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

Hopeson Khondiwa

This is very helpful

Dr. Andarge

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


  • What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

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  • Print Friendly
  • 6.4 Ethical Issues in Marketing Research
  • 1 Unit Introduction
  • In the Spotlight
  • 1.1 Marketing and the Marketing Process
  • 1.2 The Marketing Mix and the 4Ps of Marketing
  • 1.3 Factors Comprising and Affecting the Marketing Environment
  • 1.4 Evolution of the Marketing Concept
  • 1.5 Determining Consumer Needs and Wants
  • 1.6 Customer Relationship Management (CRM)
  • 1.7 Ethical Marketing
  • Chapter Summary
  • Applied Marketing Knowledge: Discussion Questions
  • Critical Thinking Exercises
  • Building Your Personal Brand
  • What Do Marketers Do?
  • Marketing Plan Exercise
  • Closing Company Case
  • 2.1 Developing a Strategic Plan
  • 2.2 The Role of Marketing in the Strategic Planning Process
  • 2.3 Purpose and Structure of the Marketing Plan
  • 2.4 Marketing Plan Progress Using Metrics
  • 2.5 Ethical Issues in Developing a Marketing Strategy
  • 2 Unit Introduction
  • 3.1 Understanding Consumer Markets and Buying Behavior
  • 3.2 Factors That Influence Consumer Buying Behavior
  • 3.3 The Consumer Purchasing Decision Process
  • 3.4 Ethical Issues in Consumer Buying Behavior
  • 4.1 The Business-to-Business (B2B) Market
  • 4.2 Buyers and Buying Situations in a B2B Market
  • 4.3 Major Influences on B2B Buyer Behavior
  • 4.4 Stages in the B2B Buying Process
  • 4.5 Ethical Issues in B2B Marketing
  • 5.1 Market Segmentation and Consumer Markets
  • 5.2 Segmentation of B2B Markets
  • 5.3 Segmentation of International Markets
  • 5.4 Essential Factors in Effective Market Segmentation
  • 5.5 Selecting Target Markets
  • 5.6 Product Positioning
  • 5.7 Ethical Concerns and Target Marketing
  • 6.1 Marketing Research and Big Data
  • 6.2 Sources of Marketing Information
  • 6.3 Steps in a Successful Marketing Research Plan
  • 7.1 The Global Market and Advantages of International Trade
  • 7.2 Assessment of Global Markets for Opportunities
  • 7.3 Entering the Global Arena
  • 7.4 Marketing in a Global Environment
  • 7.5 Ethical Issues in the Global Marketplace
  • 8.1 Strategic Marketing: Standardization versus Adaptation
  • 8.2 Diversity and Inclusion Marketing
  • 8.3 Multicultural Marketing
  • 8.4 Marketing to Hispanic, Black, and Asian Consumers
  • 8.5 Marketing to Sociodemographic Groups
  • 8.6 Ethical Issues in Diversity Marketing
  • 3 Unit Introduction
  • 9.1 Products, Services, and Experiences
  • 9.2 Product Items, Product Lines, and Product Mixes
  • 9.3 The Product Life Cycle
  • 9.4 Marketing Strategies at Each Stage of the Product Life Cycle
  • 9.5 Branding and Brand Development
  • 9.6 Forms of Brand Development, Brand Loyalty, and Brand Metrics
  • 9.7 Creating Value through Packaging and Labeling
  • 9.8 Environmental Concerns Regarding Packaging
  • 9.9 Ethical Issues in Packaging
  • 10.1 New Products from a Customer’s Perspective
  • 10.2 Stages of the New Product Development Process
  • 10.3 The Use of Metrics in Evaluating New Products
  • 10.4 Factors Contributing to the Success or Failure of New Products
  • 10.5 Stages in the Consumer Adoption Process for New Products
  • 10.6 Ethical Considerations in New Product Development
  • 11.1 Classification of Services
  • 11.2 The Service-Profit Chain Model and the Service Marketing Triangle
  • 11.3 The Gap Model of Service Quality
  • 11.4 Ethical Considerations in Providing Services
  • 12.1 Pricing and Its Role in the Marketing Mix
  • 12.2 The Five Critical Cs of Pricing
  • 12.3 The Five-Step Procedure for Establishing Pricing Policy
  • 12.4 Pricing Strategies for New Products
  • 12.5 Pricing Strategies and Tactics for Existing Products
  • 12.6 Ethical Considerations in Pricing
  • 13.1 The Promotion Mix and Its Elements
  • 13.2 The Communication Process
  • 13.3 Integrated Marketing Communications
  • 13.4 Steps in the IMC Planning Process
  • 13.5 Ethical Issues in Marketing Communication
  • 14.1 Advertising in the Promotion Mix
  • 14.2 Major Decisions in Developing an Advertising Plan
  • 14.3 The Use of Metrics to Measure Advertising Campaign Effectiveness
  • 14.4 Public Relations and Its Role in the Promotion Mix
  • 14.5 The Advantages and Disadvantages of Public Relations
  • 14.6 Ethical Concerns in Advertising and Public Relations
  • 15.1 Personal Selling and Its Role in the Promotion Mix
  • 15.2 Classifications of Salespeople Involved in Personal Selling
  • 15.3 Steps in the Personal Selling Process
  • 15.4 Management of the Sales Force
  • 15.5 Sales Promotion and Its Role in the Promotion Mix
  • 15.6 Main Types of Sales Promotion
  • 15.7 Ethical Issues in Personal Selling and Sales Promotion
  • 16.1 Traditional Direct Marketing
  • 16.2 Social Media and Mobile Marketing
  • 16.3 Metrics Used to Evaluate the Success of Online Marketing
  • 16.4 Ethical Issues in Digital Marketing and Social Media
  • 17.1 The Use and Value of Marketing Channels
  • 17.2 Types of Marketing Channels
  • 17.3 Factors Influencing Channel Choice
  • 17.4 Managing the Distribution Channel
  • 17.5 The Supply Chain and Its Functions
  • 17.6 Logistics and Its Functions
  • 17.7 Ethical Issues in Supply Chain Management
  • 18.1 Retailing and the Role of Retailers in the Distribution Channel
  • 18.2 Major Types of Retailers
  • 18.3 Retailing Strategy Decisions
  • 18.4 Recent Trends in Retailing
  • 18.5 Wholesaling
  • 18.6 Recent Trends in Wholesaling
  • 18.7 Ethical Issues in Retailing and Wholesaling
  • 19.1 Sustainable Marketing
  • 19.2 Traditional Marketing versus Sustainable Marketing
  • 19.3 The Benefits of Sustainable Marketing
  • 19.4 Sustainable Marketing Principles
  • 19.5 Purpose-Driven Marketing

Learning Outcomes

By the end of this section, you will be able to:

  • 1 Describe ethical issues relating to marketing research.
  • 2 Discuss ways to avoid unethical research practices.

The Use of Deceptive Practices

In marketing research, there are many potential areas of ethical concern. Each day people share personal information on social media, through company databases, and on mobile devices. So how do companies make sure to remain ethical in decisions when it comes to this vast amount of research data? It is essential that marketers balance the benefits of having access to this data with the privacy of and concern for all people they can impact.

Too many times, we have heard about the lack of ethical decision-making when it comes to marketing research or personal data. Companies are hacked, share or sell personal information, or use promotion disguised as research. Each of these can be considered unethical.

Link to Learning

The insights association.

There is an organization devoted to the support and integrity of quality marketing research. This organization, called The Insights Association (IA) , “protects and creates demand for the evolving insights and analytics industry by promoting the indisputable role of insights in driving business impact.” 21 Having a solid understanding of ethical practices is critical for any marketing professional. Become familiar with terminology, responsibilities, enforcements, and sanctions of the IA’s code of standards and ethics .

First, let’s look at some deceptive practices that might be conducted through research. The first is representing something as research when it is really an attempt to sell a product. This is called sugging. Sugging happens when an individual identifies themselves as a researcher, collects some data, and then uses the data to suggest specific purchases. 22 According to the Insights Association Code of Marketing Research Standards, researchers should always separate selling of products from the research process. 23

Other deceptive research practices include using persuasive language to encourage a participant to select a particular answer, misrepresenting research data subjectively rather than objectively while presenting the results, and padding research data with fabricated answers in order to increase response rate or create a specific outcome.

Invasion of Privacy

Privacy is another concern when it comes to marketing research data. For researchers, privacy is maintaining the data of research participants discretely and holding confidentiality. Many participants are hesitant to give out identifying information for fear that the information will leak, be tied back to them personally, or be used to steal their identity. To help respondents overcome these concerns, researchers can identify the research as being either confidential or anonymous.

Confidential data is when respondents share their identifying information with the researcher, but the researcher does not share it beyond that point. In this situation, the research may need some identifier in order to match up previous information with the new content—for instance, a customer number or membership number. Anonymous data is when a respondent does not provide identifying information at all, so there is no chance of being identified. Researchers should always be careful with personal information, keeping it behind a firewall, behind a password-protected screen, or physically locked away.

Breaches of Confidentiality

One of the most important ethical considerations for marketing researchers is the concept of confidentiality of respondents’ information. In order to have a rich data set of information, very personal information may be gathered. When a researcher uses that information in an unethical manner, it is a breach of confidentiality . Many research studies start with a statement of how the respondent’s information will be used and how the researcher will maintain confidentiality. Companies may sell personal information, share contact information of the respondents, or tie specific answers to a respondent. These are all breaches of the confidentiality that researchers are held accountable for. 24

Although we hear about how companies are utilizing customers’ data unethically, many companies operate in an ethical manner. One example is the search engine DuckDuckGo . The search industry generates millions of pieces of user data daily; most of the providers of searches capitalize on this data by tracking and selling this information. Alternatively, DuckDuckGo has decided NOT to track its users. Instead, it has built its business model on the fact that no user information is stored—ever. Ethically, DuckDuckGo offers users private searches, tracker blocking, and site encryption. In an industry that is continuously collecting and selling personal search information, DuckDuckGo is the exception. There is no concern with being hacked because no data is collected. 25

Companies with a Conscience

The Gallup Organization is a market research firm that specializes in understanding market sentiment (see Figure 6.11 ). Every year among its numerous polls, Gallup completes an assessment of the honesty and ethical approach of different professions. In the 2021 survey, nursing was the top profession regarding these two measures. 26

Gallup’s research led additional findings about the state of ethics for businesses. “Ethical standards need to be at the core of an organization’s purpose, brand and culture.” 27 But what about Gallup’s own ethical standards? Gallup is “a global analytics and advice firm that helps leaders and organizations solve their most pressing problems.” 28 In order to be proficient and well-informed on the variety of topics Gallup investigates, it must hold itself and its employees to a high ethical standard.

Gallup completes multiple polls and research continuously. In order to meet the high standards of its public, Gallup must perform these practices in an ethical manner. Each step of the research process is completed with diligence and intention. For those reasons, Gallup is recognized for its ethically backed data. Gallup is a global leader in market insights and has locations in seven cities within the United States and an additional 27 locations internationally. According to Chuck Hagel, former Secretary of Defense of the United States, “Gallup is truly an island of independence—it possesses a credibility and trust that hardly any institution has. A reputation for impartial, fair, honest and superb work.” 29

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BUS602: Marketing Management

hypothesis of marketing research

Case Study: Role of Marketing Mix

This research article looks at tourism of Lake Samosir in North Sumatra, Indonesia. The underlying question is whether the implementation of the marketing mix influenced tourism in the area.

4.3. Hypothesis Testing

As explained in the previous chapter, there are 5 hypotheses in this study. Hypothesis testing analysis is carried out with a significance level of 5%, resulting in a critical t-value of ± 1.96. The hypothesis is accepted if the t-value obtained ≥ 1.96, while hypothesis is not supported if the t-value obtained < 1.96. The following is a table of hypothesis testing to answer the overall questions of the study:

Table 2: Hypothesis Testing of Research Model Based on table 2 above which contains the conclusion of the hypothesis model results, it can be concluded as follows:

a) Marketing Mix has a positive effect on Tourist Satisfactions

 Based on data processing results of the structural model, the output of t-value is 3.78. The result of t-value shown is greater than 1.96, so it can be concluded that the variable of marketing mix has a significant positive effect on tourist satisfactions. Thus, hypothesis 1 can be accepted and it can be concluded that the higher marketing mix perceived, the higher tourist satisfactions will be.

b) Service Quality has a positive effect on Tourist Satisfactions

Based on data processing results of the structural model, the output of t-value is 5.94. The result of t-value shown is greater than 1.96, so it can be concluded that the variable of service quality has a significant positive effect on tourist satisfactions. Thus, it can be concluded that the higher service quality perceived, the higher tourist satisfactions will be.

c) Marketing Mix has a positive effect on Tourists Loyalty

Based on data processing results of the structural model, the output of t-value is 4.19. The result of t-value shown is greater than 1.96, so it can be concluded that the variable of marketing mix has a significant positive effect on tourists loyalty. Thus, it can be concluded that the higher marketing mix perceived, the higher tourist loyalty will be.

d) Service Quality has a positive effect on Tourists Loyalty

 Based on data processing results of the structural model, the output of t-value is 3.23. The result of t-value shown is greater than 1.96, so it can be concluded that the variable of service quality has a significant positive effect on tourists loyalty. Thus, it can be concluded that the higher the perceived service quality, the higher tourist loyalty will be.

e) Satisfactions has a positive effect on Tourists Loyalty

 Based on data processing results of the structural model, the output of t-value is 3.16. The result of t-value shown is greater than 1.96, so it can be concluded that the variable of satisfactions has a significant positive effect on tourists loyalty. Thus, it can be concluded that with higher perceived satisfaction comes higher tourist loyalty.

Hypothesis Testing Of Mediation (Indirect Effects) 

 As explained in the previous chapter, in this study there are two moderation hypotheses by Tourist Satisfaction variables. Hypothesis testing analysis is carried out with a significance level of 5%, resulting in a critical t-value of ± 1.96. The hypothesis is accepted if the t-value obtained ≥ 1.96, while hypothesis is not supported if the t-value obtained < 1.96. 

  The following is a table of testing hypotheses to answer indirect influences. 

Table 3. Testing of Indirect Influence Hypotheses 

Based on the results of the LISREL output above, the data from the structural model, obtained the output of t-value (line 3), in the result showed that the variables of tourists satisfaction can mediate the effect between the variable of marketing mix and service quality that has an indirect effect on tourists loyalty. This can be seen from t-count value is greater than 1.96 i.e. 2.50 and 3.00. no longer supports Internet Explorer.

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What is a Research Hypothesis And How to Write it?

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

A research hypothesis can be defined as a clear, specific and predictive statement that states the possible outcome of a scientific study. The result of the research study is based on previous research studies and can be tested by scientific research.

The research hypothesis is written before the beginning of any scientific research or data collection .

Table of Contents

What is Research Hypothesis?

The research hypothesis is the first step and basis of all research endeavours. The research hypothesis shows what you want to prove with your research study. Therefore, the research hypothesis should be written first before you begin the study, no matter what kind of research study you are conducting.

The research hypothesis shows the direction to the researcher conducting the research. It states what the researcher expects to find from the study. It is a tentative answer that guides the entire research study.

Writing a research hypothesis is not an easy task. It requires skills to write a testable research hypothesis. The researcher is required to study the research done by other researchers on the same subject and find out the loopholes in those researches to make it the basis for their research.

Make sure to consider the general research question posed in the study before jumping directly to write a research hypothesis. Pointing out the exact question can be very difficult for researchers as most researchers are usually not aware of what they are trying to find from their research study. Moreover, the added excitement to conduct the study makes it even more difficult for the researchers to pin down the exact research hypothesis.

There are two primary criteria to develop a reasonable research hypothesis. First, the research hypothesis should be researchable and second; it must be interesting. By researchable, we mean that the question in the research hypothesis statement should be able to be answered with the help of science and the answer to the question should be answerable within a reasonable period.

The research hypothesis being interesting means that the research question should be valuable in the context of the ongoing scientific research of the topic.

Let us learn about the research hypothesis in quantitative and qualitative studies:

Research hypothesis in Quantitative studies

The research hypothesis in a quantitative study consists of one independent variable and one dependent variable, and the research hypothesis mentions the expected relationship between both of the variables.

The independent variable is mentioned first in the research hypothesis followed by explanations and results, etc. and then the dependent variable is specified. Make sure that the variables are referred to in the same order as they are mentioned in the research hypothesis; otherwise, there are chances that your readers get confused while reading your research proposal .

When both variables are used in continuous nature, then it is easy to describe negative or positive relationships between both of them. In the case of categorical variables, the hypothesis statement about which category of independent variables is associated with which group of dependent variables.

It is good to represent the research hypothesis in directional format. That means, the statement is made about the expected relationship between the variables based on past research, the study of existing research, on an educational guess , or only by observation.

Additionally, the null hypothesis can also be used between two variables which state that there is no relationship between the variables. The null hypothesis is the basis of all types of statistical research.

Lastly, a simple research hypothesis for quantitative research should provide a direction for the study of the relationship between two variables. Still, it should also use phrases like “tend to” or “in general” to soften the tone of the hypothesis.

Research hypothesis in qualitative research

The role of the research hypothesis in qualitative research is different as compared to its role in quantitative research. The research hypothesis is not developed at the beginning of the research because of the inductive nature of the qualitative studies.

The research hypothesis is introduced during the iterative process of data collection and the Interpretation of the data. The research hypothesis helps the researchers ask more questions and look for answers for disconfirming evidence.

The qualitative study is dependent on the questions and subquestions asked by the researchers at the beginning of the qualitative research. Generally, in qualitative studies one or two central questions are developed and based on these central questions a series of five to ten subquestions is built and these sub-questions are further used to develop central questions for the research purpose.

In qualitative studies, these questions are directly asked the participant of the research study usually through focus groups or in-depth interviews. This is done to develop an understanding between participants of the study and the researchers. This helps in creating a collaborative experience between the two.

Variables in hypothesis

In research studies like correlational research and experimental studies, a hypothesis shows a relationship between two or more variables. There is an independent variable and a dependent variable.

An independent variable is a variable that a researcher can control and change, whereas, a dependent variable is a variable that the researcher measures and observes.

For example, regular exercise lowers the chances of a heart attack. In this example, the regular exercise is an independent variable and probabilities of occurrence of heart attack is a dependent variable that researchers can measure by observation.

How to develop a reasonable research hypothesis?

How to develop a reasonable research hypothesis

A research hypothesis plays an essential role in the research study. Therefore, it is necessary to develop an accurate and precise research hypothesis. In this section, you will learn how to develop a reasonable research hypothesis. The following are the steps involved in developing a research hypothesis.

Step 1. Have a question?

The first step involved in writing a research hypothesis is having a question that you want to answer. This question should be specific and within the scope of your research area. Make sure that the question that you ask is researchable within the time duration of your research study. The examples of research hypothesis questions can be

  • Do students who attend classes regularly score more in exams?
  • Do people prefer to buy products that have a high price as compared to the other similar products available in the market ?

Step 2. Do some preliminary research:

Preliminary research is conducted before a researcher decides his research hypothesis. In the preliminary research, all the knowledge available about the question is collected by studying the theories and previous studies.

Having this knowledge helps the researchers to form educational assumptions about the outcomes of the research. At this stage, the researcher might prepare a conceptual framework to determine which variable should be studied and what you think is the relationship between the different variables.

The preliminary study also helps the researcher to change the topic if he feels the problem doesn’t have much scope for research.

Step 3. Formulation of hypothesis:

At this stage, the final research hypothesis is formulated. At this stage, the researcher has some idea of what he should expect from the research study. Write the answer to the question of research hypothesis in concise and clear sentences.

The clearer the research hypothesis, the easier will be for researchers to conduct the research.

Step 4. Refine the final hypothesis:

It is essential to make sure that your research hypothesis is testable and specific. You can define a hypothesis in different ways, but you should make sure that all the words that you use in your research hypothesis have precise definitions.

Besides, your hypothesis should contain a set of variables, the relationship between the variables, specific group being studied, and already predicted the outcome of the research.

Step 5. Use three methods to phrase your hypothesis:

They establish a clear relationship between variables, write the hypothesis in if.. then form. The first part of the sentence should be an independent variable, and the second part of the variable should state the dependent variable.

For example, if a student attends 100% classes in a semester, then he will score more than 90% in the exams.

In academic research, the research hypotheses are formed in terms of correlations or effects. In such hypotheses, the relationship between the variables is directly stated in the research hypothesis.

For example, the high numbers of lectures attended by students have a positive impact on their results.

When you are writing a research hypothesis to compare two groups, the hypothesis should state what the differences you are expecting to find in both the groups are.

For example, the students who have more than 70% attendance will score better in exams than the students who have lower than 50% attendance.

Step 6. Write the Null hypothesis:

A null hypothesis is written when research involves statistical hypothesis testing. A null hypothesis when there is no specific relationship between the variables.

It is a default position that shows that two variables used in the hypothesis are not related to each other. A null hypothesis is usually written as H0, and alternative hypotheses are written as H1 or Ha.

Importance of Research Hypothesis

Research plays an essential role in every field. To experiment, a researcher needs to make sure that the research he wants to conduct is testable. A research hypothesis is developed after conducting a preliminary study.

A preliminary study is the study of previous studies done by researchers and the study of research papers written on the same concept. With the help of the research hypothesis, a researcher makes sure that he is not hidden towards a dead end, and it works as a direction map for the researcher.

Liked this post? Check out the complete series on Market research

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About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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30 Marketing Theories And Frameworks In A Nutshell

Table of Contents

Six Forces Models


SWOT Analysis


Balanced Scorecard


Marketing Mix


PESTEL Analysis


Maslow’s Hierarchy of Needs


GE McKinsey Matrix


Kotler’s Five Product Levels


New Product Development Process


Customer Experience Map


Social Selling


CHAMP Methodology


BANT Sales Process


MEDDIC Sales Process


STP Marketing


Sales Funnels vs. Flywheels


Pirate Metrics




Customer Segmentation


Real-Time Marketing


Bullseye Framework


Affinity Marketing


Brand Marketing


Project Portfolio Management


Brand Storytelling


Brand Essence


Meme Marketing


Key Highlights

  • Six Forces Model: The Six Forces Model is an adaptation of Porter’s Five Forces, adding the sixth force of complementary products. It is used in the tech business world to assess the impact of new market entrants and potential substitutes, especially in the context of complementary products.
  • SWOT Analysis: A SWOT Analysis is a framework used to evaluate a business’s Strengths, Weaknesses, Opportunities, and Threats. It helps identify problematic areas, maximize opportunities, and anticipate future challenges.
  • Balanced Scorecard: Developed by Robert Kaplan, the balanced scorecard is a management system focusing on big-picture strategic goals. It includes four perspectives: financial, customer, business process, and organizational capacity, providing a holistic view of the business.
  • Marketing Mix: The marketing mix refers to a multi-faceted approach for a complete and effective marketing plan. Traditionally, it includes the four Ps: price, product, promotion, and place, with newer additions like physical evidence, people, process, and politics.
  • PESTEL Analysis: The PESTEL analysis is a framework to assess macro-economic factors affecting an organization. It helps identify potential threats and weaknesses, providing a broader understanding of the marketing environment.
  • BCG Matrix: The BCG Matrix categorizes a product portfolio into cash cows, pets (dogs), question marks, and stars, based on their potential growth and market shares.
  • Maslow’s Hierarchy of Needs: Developed by Abraham Maslow, this hierarchy explains human needs and desires. In marketing , it’s used to target specific groups based on their needs, desires, and actions.
  • PESO Model: The PESO model categorizes media into paid, earned, shared, and owned media, useful for content-driven, online marketing strategies.
  • GE McKinsey Matrix: This matrix guides a corporation on how to prioritize investments among business units, leading to scenarios like invest, protect, harvest, and divest.
  • Kotler’s Five Product Levels: This model identifies five types of products: core product, generic product, expected product, augmented product, and potential product, based on the satisfaction of consumer needs.
  • New Product Development Process: This process goes from idea generation to post-launch review, helping companies analyze aspects of launching new products.
  • Customer Experience Map: Visual representations of customer interactions with a brand, including touchpoints, to enhance customer engagement.
  • AIDA Model: The AIDA model describes the potential journey a customer goes through before purchasing a product: attention, interest, desire, and action.
  • Social Selling: The process of developing trust and rapport with prospects through social platforms before closing sales.
  • CHAMP Methodology: An iteration of the BANT sales process for modern B2B applications.
  • BANT Sales Process: A process to quickly identify prospects most likely to make a purchase: budget, authority, need, and timing.
  • MEDDIC Sales Process: A framework used by B2B sales teams to foster predictable and efficient growth.
  • STP Marketing: A common approach in modern marketing , focusing on commercial effectiveness by selecting valuable target segments and developing a positioning strategy and marketing mix for each.
  • Sales Funnels vs. Flywheels: Models representing the customer journey and structuring sales and marketing tactics.
  • Pirate Metrics (AARRR): Metrics and channels to look at during the customer journey toward becoming customers and referrers.
  • Bootstrapping: Financing growth from available cash flows in a viable business model.
  • Customer Segmentation: Identifying different groups of people a business hopes to reach and serve.
  • Real-Time Marketing: In-the-moment marketing to customers based on interactions with the brand.
  • Affinity Marketing: Partnerships between businesses to sell more products, benefiting both brands.
  • Brand Marketing: Building a relationship between the brand and customers, promoting the brand as a whole.
  • Project Portfolio Management: A systematic approach to selecting and managing projects aligned with organizational objectives.
  • Brand Storytelling: Using authentic, emotion-driven narratives to promote brand growth and customer loyalty.
  • Brand Essence: Templated approach to understand the brand based on attributes, benefits, values, personality, and essence.
  • Meme Marketing: Using memes to promote a brand, leveraging viral internet content.

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  1. Chapter 1

    hypothesis of marketing research

  2. Expert Advice on Developing a Hypothesis for Marketing Experimentation

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  3. Hypotheses & Assumptions: Add a Sprinkle of Science to Your Marketing

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  4. How to write a hypothesis for marketing experimentation

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  5. The Marketing Research Process

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  6. Marketing Research Hypothesis Examples

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  1. Hypothesis Tests and Dependent T Tests

  2. Marketing Research

  3. Hypothesis ( Subject

  4. Hypothesis in Research

  5. What is Hypothesis

  6. Theory and Hypothesis with Practical Applications


  1. How to write a hypothesis for marketing experimentation

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