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• THE NATURE AND IMPORTANCE OF MARKETING RESEARCH

Informal and, by today's standards, crude attempts to analyze the market date back to the earliest days of the marketing revolution. Only in recent years, however, has the role of research as it relates to management been clearly recognized.

Reflecting this change in orientation, the following definition of marketing research is offered: marketing research is the scientific and controlled gathering of nonroutine marketing information undertaken to help management solve marketing problems. There is often hearty disagreement over the answer to the question of whether marketing research is a science. One's answer depends on the employed definition of "science". To be specific, a research activity should use the scientific method. In this method, hypotheses (tentative statements of relationships or of solutions to problems) are drawn from informal observations. These hypotheses are then tested. Ultimately, the hypothesis is accepted, rejected, or modified according to the results of the test. In a true science, verified hypotheses are turned into "laws." In marketing research, verified hypotheses become the generalizations upon which management develops its marketing programs. (To simplify our discussion, we will use "questions" as a synonym of "hypothesis".)

The mechanics of marketing research must be controlled so that the right facts are obtained in the answer to the correct problem. The control of fact-finding is the responsibility of the research director, who must correctly design the research and carefully supervise its execution to ensure it goes according to plan. Maintaining control in marketing research is often difficult because of the distance that separates the researcher and the market and because the services of outsiders are often required to complete a research project. 1

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Expert Advice on Developing a Hypothesis for Marketing Experimentation 

• Conversion Rate Optimization

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.

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 : 

Visitors. 

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.  

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: 

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:

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.”

Conclusion 

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.

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Much of running a small business is a gamble, buoyed by boldness, intuition and guts. But wise business leaders also conduct formal and informal research to inform their business decisions. Good research starts with a good hypothesis, which is simply a statement making a prediction based on a set of observations. For example, if you’re considering offering flexible work hours to your employees, you might hypothesize that this policy change will positively affect their productivity and contribute to your bottom line. The ultimate job of the hypothesis in business is to serve as a guidepost to your testing and research methods.

Importance of Hypothesis Testing in Business

Essentially good hypotheses lead decision-makers like you to new and better ways to achieve your business goals. When you need to make decisions such as how much you should spend on advertising or what effect a price increase will have your customer base, it’s easy to make wild assumptions or get lost in analysis paralysis. A business hypothesis solves this problem, because, at the start, it’s based on some foundational information. In all of science, hypotheses are grounded in theory. Theory tells you what you can generally expect from a certain line of inquiry.

A hypothesis based on years of business research in a particular area, then, helps you focus, define and appropriately direct your research. You won’t go on a wild goose chase to prove or disprove it. A hypothesis predicts the relationship between two variables. If you want to study pricing and customer loyalty, you won’t waste your time and resources studying tangential areas.

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One of the most important hypotheses you’ll make in growing your small business is the cost of acquiring a customer. Your viability as a business is founded on ensuring that your customers bring you more money than it costs you to get them in the door. Hypothesizing this number informs not only your pricing strategy but also your marketing efforts and the rest of your overhead expenses. You can also make predictions about the lifetime value of each customer to determine how much marketing you need to do. Businesses frequently attempt to guesstimate how long a customer will stick around and how much sales to each one will contribute to your profit.

In real life, hypotheses are honed and perfected over time through refining of your basic questions, assumptions and research methods, suggests Quickbooks. In addition, you may have more than one hypothesis to explain your observations, such as why your product failed or why morale is sinking in the office.

Forming a Hypothesis

To form a good hypothesis, you should ensure certain criteria are met when making your prediction statements. The hypothesis must be testable as a start, reports Corporate Finance Institute . Don’t make the mistake of trying to prove a tautology, or a hypothesis that is always true. For example, “Our social media strategy will succeed if it’s social or it will fail.” In addition, your hypothesis should be based on the most up-to-date research and knowledge on the subject matter.

Don't Forget to Test It

The most important part of having a hypothesis is determining whether it’s supported by the facts. The scope and formality of your research depend on your research and may simply involve examining the literature, polling your stakeholders or researching other areas. For example, in determining whether to locate your business in a pricey downtown or an exurb with no public transportation, you may look at commuting statistics of your general metropolitan area, the prevalence of carpooling, the socioeconomic status of most of your employees, as well as where your competitors are located.

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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team who historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learning How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Are you interested in improving your data literacy? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.

About the Author

How to write a hypothesis for marketing experimentation

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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• Published: May 2001

Hypotheses in Marketing Science: Literature Review and Publication Audit

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

Marketing Letters volume  12 ,  pages 171–187 ( 2001 ) Cite this article

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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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Armstrong, J.S., Brodie, R.J. & Parsons, A.G. Hypotheses in Marketing Science: Literature Review and Publication Audit. Marketing Letters 12 , 171–187 (2001). https://doi.org/10.1023/A:1011169104290

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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 August 15, 2023.

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

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

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

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

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

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

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

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

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

 Statistics

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

Research bias

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

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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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Understanding the importance of a research hypothesis

A research hypothesis is a specification of a testable prediction about what a researcher expects as the outcome of the study. It comprises certain aspects such as the population, variables, and the relationship between the variables. It states the specific role of the position of individual elements through empirical verification. When conducting research, there are certain assumptions that are made by the researcher. According to the available information, the goal is to present the expected outcome after testing them.

A hypothesis should be precise and accurate

A hypothesis is a clear statement of the information that the researcher intends to investigate. It is thus a clear statement that is essential before conducting research.

Based on this aspect, the features of the hypothesis are listed below:

1. Conceptual

The statement of the hypothesis is based on a certain concept i.e. it could be either related to the theory or the pre-assumption of the researcher about certain variables i.e. educated guess. This leads to linking the research questions of the study. It helps the collection of data and conducting analysis as per the stated concept.

People who shop at speciality stores tend to spend more on luxury brands as compared to those who shop at a department store.

2. Verbal statement

The research hypothesis represents a verbal statement in declarative form. The hypothesis is often stated in mathematical form. However, it brings in the possibility of representing the idea, assumption, or concept of the researcher in the form of words that could be tested.

The capability of students who are undergoing vocational training programs is not different from the students undergoing regular studies.

3. Empirical reference

By building a tentative relationship among concepts, hypothesis testing provides an empirical verification of a study. It helps validate the assumption of the researcher.

The quality of nursing education affects the quality of nursing practice skills.

4. Tentative relationship

It links the variables as per assumption and builds a tentative relationship. A hypothesis is initially unverified, therefore the relationship between variables is uncertain. Thus a predictable relationship is specified.

Sleep deprivation affects the productivity of an individual.

5. Tool of knowledge advancement

With help of a hypothesis statement, the researcher has the opportunity of verifying the available knowledge and having further enquiry about a concept. Thus, it helps the advancement of knowledge.

The effectiveness of social awareness programs influences the living standards of people.

The hypothesis statement provides the benefit of assessing the available information and making the appropriate prediction about the future. With the possibility of verifiability and identifying falsifiable information, researchers assess their assumptions and determine accurate conclusions.

People who are exposed to a high level of ultraviolet light tend to have a higher incidence of cancer.

7. Not moral

The hypothesis statement is not based on the consideration of moral values or ethics. It is as per the beliefs or assumptions of the researcher. However, testing and prediction are not entirely based on individual moral beliefs. For example, people having sample moral values would take the same strategy for business management. In this case, it is not the desired objective to study the business management strategy.

Neither too specific nor too general

A hypothesis should not be too general or too specific.

‘Actions of an individual would impact the health’ is too general, and ‘running would improve your health’ is too specific. Thus, the hypothesis for the above study is exercise does have an impact on the health of people.

Prediction of consequences

The hypothesis is the statement of the researcher’s assumption. Thus, it helps in predicting the ultimate outcome of the thesis.

Experience leads to better air traffic control management.

Even if the assumption of the researcher is proven false in testing, the result derived from the examination is valuable. With the presence of null and alternative hypotheses, each assessment of the hypothesis yields a valuable conclusion.

Separating irrelevant information from relevant information

 A hypothesis plays a significant role ineffectiveness of a study. It not only navigates the researcher but also prevents the researcher from building an inconclusive study. By guiding as light in the entire thesis, the hypothesis contributes to suggesting and testing the theories along with describing the legal or social phenomenon.

Navigate research

A hypothesis helps in identifying the areas that should be focused on for solving the research problem. It helps frame the concepts of study in a meaningful and effective manner. It also helps the researcher arrive at a conclusion for the study based on organized empirical data examination.

Prevents blind research

A hypothesis guides the researcher in the processes that need to be followed throughout the study. It prevents the researcher from collecting massive data and doing blind research which would prove irrelevant.

A platform for investigating activities

By examining conceptual and factual elements related to the problem of a thesis, the hypothesis provides a framework for drawing effective conclusions. It also helps stimulate further studies.

Describes a phenomenon

Each time a hypothesis is tested, more information about the concerned phenomenon is made available. Empirical support via hypothesis testing helps analyse aspects that were unexplored earlier.

Framing accurate research hypothesis statements

For the deduction of accurate and reliable outcomes from the analysis, belong stated things should be noted:

• Should never be formulated in the form of a question.
• Empirical testability of the hypothesis should be possible.
• A precise and specific statement of concept should be present.
• The hypothesis should not be contradictory to the identified concept and linkage between the variables.
• A clear specification of all the variables which are used for building relationships in the hypothesis should be present.
• The focus of a single hypothesis should only be on one issue. No multi-issue consideration should be taken while building the hypothesis i.e. could only be either relational or descriptive.
• The hypothesis should not be conflicting with the defined law of nature which is already specified as true.
• Effective tools and techniques need to be used for the verification of the hypothesis.
• The form of the hypothesis statement should be simple and understandable. Complex or conflicting statement reduces the applicability and reliability of the thesis results.
• The hypothesis should be amendable in the form that testing could be completed within a specified reasonable time.
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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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Module 6: Marketing Information and Research

The marketing research process, learning objectives.

• Identify the steps of conducting a marketing research project

A Standard Approach to Research Inquiries

Marketing research is a useful and necessary tool for helping marketers and an organization’s executive leadership make wise decisions. Carrying out marketing research can involve highly specialized skills that go deeper than the information outlined in this module. However, it is important for any marketer to be familiar with the basic procedures and techniques of marketing research.

It is very likely that at some point a marketing professional will need to supervise an internal marketing research activity or to work with an outside marketing research firm to conduct a research project. Managers who understand the research function can do a better job of framing the problem and critically appraising the proposals made by research specialists. They are also in a better position to evaluate their findings and recommendations.

Periodically marketers themselves need to find solutions to marketing problems without the assistance of marketing research specialists inside or outside the company. If you are familiar with the basic procedures of marketing research, you can supervise and even conduct a reasonably satisfactory search for the information needed.

Step 1: Identify the Problem

The first step for any marketing research activity is to clearly identify and define the problem you are trying to solve. You start by stating the marketing or business problem you need to address and for which you need additional information to figure out a solution. Next, articulate the objectives for the research: What do you want to understand by the time the research project is completed? What specific information, guidance, or recommendations need to come out of the research in order to make it a worthwhile investment of the organization’s time and money?

It’s important to share the problem definition and research objectives with other team members to get their input and further refine your understanding of the problem and what is needed to solve it. At times, the problem you really need to solve is not the same problem that appears on the surface. Collaborating with other stakeholders helps refine your understanding of the problem, focus your thinking, and prioritize what you hope to learn from the research. Prioritizing your objectives is particularly helpful if you don’t have the time or resources to investigate everything you want.

To flesh out your understanding of the problem, it’s useful to begin brainstorming actual research questions you want to explore. What are the questions you need to answer in order to get to the research outcomes? What is the missing information that marketing research will help you find? The goal at this stage is to generate a set of preliminary, big-picture questions that will frame your research inquiry. You will revisit these research questions later in the process, but when you’re getting started, this exercise helps clarify the scope of the project, whom you need to talk to, what information may already be available, and where to look for the information you don’t yet have.

Applied Example: Marketing Research for Bookends

To illustrate the marketing research process, let’s return to Uncle Dan and his ailing bookstore, Bookends. You need a lot of information if you’re going to help Dan turn things around, so marketing research is a good idea. You begin by identifying the problem and then work to set down your research objectives and initial research questions:

Step 2: Develop a Research Plan

Once you have a problem definition, research objectives, and a preliminary set of research questions, the next step is to develop a research plan. Essential to this plan is identifying precisely what information you need to answer your questions and achieve your objectives. Do you need to understand customer opinions about something? Are you looking for a clearer picture of customer needs and related behaviors? Do you need sales, spending, or revenue data? Do you need information about competitors’ products, or insight about what will make prospective customers notice you? When do need the information, and what’s the time frame for getting it? What budget and resources are available?

Once you have clarified what kind of information you need and the timing and budget for your project, you can develop the research design. This details how you plan to collect and analyze the information you’re after. Some types of information are readily available through  secondary research and secondary data sources. Secondary research analyzes information that has already been collected for another purpose by a third party, such as a government agency, an industry association, or another company. Other types of information need to from talking directly to customers about your research questions. This is known as primary research , which collects primary data captured expressly for your research inquiry.   Marketing research projects may include secondary research, primary research, or both.

Depending on your objectives and budget, sometimes a small-scale project will be enough to get the insight and direction you need. At other times, in order to reach the level of certainty or detail required, you may need larger-scale research involving participation from hundreds or even thousands of individual consumers. The research plan lays out the information your project will capture—both primary and secondary data—and describes what you will do with it to get the answers you need. (Note: You’ll learn more about data collection methods and when to use them later in this module.)

Your data collection plan goes hand in hand with your analysis plan. Different types of analysis yield different types of results. The analysis plan should match the type of data you are collecting, as well as the outcomes your project is seeking and the resources at your disposal. Simpler research designs tend to require simpler analysis techniques. More complex research designs can yield powerful results, such as understanding causality and trade-offs in customer perceptions. However, these more sophisticated designs can require more time and money to execute effectively, both in terms of data collection and analytical expertise.

The research plan also specifies who will conduct the research activities, including data collection, analysis, interpretation, and reporting on results. At times a singlehanded marketing manager or research specialist runs the entire research project. At other times, a company may contract with a marketing research analyst or consulting firm to conduct the research. In this situation, the marketing manager provides supervisory oversight to ensure the research delivers on expectations.

Finally, the research plan indicates who will interpret the research findings and how the findings will be reported. This part of the research plan should consider the internal audience(s) for the research and what reporting format will be most helpful. Often, senior executives are primary stakeholders, and they’re anxious for marketing research to inform and validate their choices. When this is the case, getting their buy-in on the research plan is recommended to make sure that they are comfortable with the approach and receptive to the potential findings.

Applied Example: A Bookends Research Plan

You talk over the results of your problem identification work with Dan. He thinks you’re on the right track and wants to know what’s next. You explain that the next step is to put together a detailed plan for getting answers to the research questions.

Dan is enthusiastic, but he’s also short on money. You realize that such a financial constraint will limit what’s possible, but with Dan’s help you can do something worthwhile. Below is the research plan you sketch out:

Step 3: Conduct the Research

Conducting research can be a fun and exciting part of the marketing research process. After struggling with the gaps in your knowledge of market dynamics—which led you to embark on a marketing research project in the first place—now things are about to change. Conducting research begins to generate information that helps answer your urgent marketing questions.

Typically data collection begins by reviewing any existing research and data that provide some information or insight about the problem. As a rule, this is secondary research. Prior research projects, internal data analyses, industry reports, customer-satisfaction survey results, and other information sources may be worthwhile to review. Even though these resources may not answer your research questions fully, they may further illuminate the problem you are trying to solve. Secondary research and data sources are nearly always cheaper than capturing new information on your own. Your marketing research project should benefit from prior work wherever possible.

After getting everything you can from secondary research, it’s time to shift attention to primary research, if this is part of your research plan. Primary research involves asking questions and then listening to and/or observing the behavior of the target audience you are studying. In order to generate reliable, accurate results, it is important to use proper scientific methods for primary research data collection and analysis. This includes identifying the right individuals and number of people to talk to, using carefully worded surveys or interview scripts, and capturing data accurately.

Without proper techniques, you may inadvertently get bad data or discover bias in the responses that distorts the results and points you in the wrong direction. The module on Marketing Research Techniques discusses these issues in further detail, since the procedures for getting reliable data vary by research method.

Applied Example: Getting the Data on Bookends

Dan is on board with the research plan, and he’s excited to dig into the project. You start with secondary data, getting a dump of Dan’s sales data from the past two years, along with related information: customer name, zip code, frequency of purchase, gender, date of purchase, and discounts/promotions (if any).

You visit the U.S. Census Bureau Web site to download demographic data about your metro area. The data show all zip codes in the area, along with population size, gender breakdown, age ranges, income, and education levels.

The next part of the project is customer-survey data. You work with Dan to put together a short survey about customer attitudes toward Bookends, how often and why they come, where else they spend money on books and entertainment, and why they go other places besides Bookends. Dan comes up with the great idea of offering a 5 percent discount coupon to anyone who completes the survey. Although it eats into his profits, this scheme gets more people to complete the survey and buy books, so it’s worth it.

For a couple of days, you and Dan take turns doing “man on the street” interviews (you interview the guy in the red hat, for instance). You find people who say they’ve never been to Bookends and ask them a few questions about why they haven’t visited the store, where else they buy books and other entertainment, and what might get them interested in visiting Bookends sometime. This is all a lot of work, but for a zero-budget project, it’s coming together pretty well.

Step 4: Analyze and Report Findings

Analyzing the data obtained in a market survey involves transforming the primary and/or secondary data into useful information and insights that answer the research questions. This information is condensed into a format to be used by managers—usually a presentation or detailed report.

Analysis starts with formatting, cleaning, and editing the data to make sure that it’s suitable for whatever analytical techniques are being used. Next, data are tabulated to show what’s happening: What do customers actually think? What’s happening with purchasing or other behaviors? How do revenue figures actually add up? Whatever the research questions, the analysis takes source data and applies analytical techniques to provide a clearer picture of what’s going on. This process may involve simple or sophisticated techniques, depending on the research outcomes required. Common analytical techniques include regression analysis to determine correlations between factors; conjoint analysis to determine trade-offs and priorities; predictive modeling to anticipate patterns and causality; and analysis of unstructured data such as Internet search terms or social media posts to provide context and meaning around what people say and do.

Good analysis is important because the interpretation of research data—the “so what?” factor—depends on it. The analysis combs through data to paint a picture of what’s going on. The interpretation goes further to explain what the research data mean and make recommendations about what managers need to know and do based on the research results. For example, what is the short list of key findings and takeaways that managers should remember from the research? What are the market segments you’ve identified, and which ones should you target?  What are the primary reasons your customers choose your competitor’s product over yours, and what does this mean for future improvements to your product?

Individuals with a good working knowledge of the business should be involved in interpreting the data because they are in the best position to identify significant insights and make recommendations from the research findings. Marketing research reports incorporate both analysis and interpretation of data to address the project objectives.

The final report for a marketing research project may be in written form or slide-presentation format, depending on organizational culture and management preferences. Often a slide presentation is the preferred format for initially sharing research results with internal stakeholders. Particularly for large, complex projects, a written report may be a better format for discussing detailed findings and nuances in the data, which managers can study and reference in the future.

Applied Example: Analysis and Insights for Bookends

Getting the data was a bit of a hassle, but now you’ve got it, and you’re excited to see what it reveals. Your statistician cousin, Marina, turns out to be a whiz with both the sales data and the census data. She identified several demographic profiles in the metro area that looked a lot like lifestyle segments. Then she mapped Bookends’ sales data into those segments to show who is and isn’t visiting Bookends. After matching customer-survey data to the sales data, she broke down the segments further based on their spending levels and reasons they visit Bookends.

Gradually a clearer picture of Bookends’ customers is beginning to emerge: who they are, why they come, why they don’t come, and what role Bookends plays in their lives. Right away, a couple of higher-priority segments—based on their spending levels, proximity, and loyalty to Bookends—stand out. You and your uncle are definitely seeing some possibilities for making the bookstore a more prominent part of their lives. You capture these insights as “recommendations to be considered” while you evaluate the right marketing mix for each of the new segments you’d like to focus on.

Step 5: Take Action

Once the report is complete, the presentation is delivered, and the recommendations are made, the marketing research project is over, right? Wrong.

What comes next is arguably the most important step of all: taking action based on your research results.

If your project has done a good job interpreting the findings and translating them into recommendations for the marketing team and other areas of the business, this step may seem relatively straightforward. When the research results validate a path the organization is already on, the “take action” step can galvanize the team to move further and faster in that same direction.

Things are not so simple when the research results indicate a new direction or a significant shift is advisable. In these cases, it’s worthwhile to spend time helping managers understand the research, explain why it is wise to shift course, and explain how the business will benefit from the new path. As with any important business decision, managers must think deeply about the new approach and carefully map strategies, tactics, and available resources to plan effectively. By making the results available and accessible to managers and their execution teams, the marketing research project can serve as an ongoing guide and touchstone to help the organization plan, execute, and adjust course as it works toward desired goals and outcomes.

It is worth mentioning that many marketing research projects are never translated into management action. Sometimes this is because the report is too technical and difficult to understand. In other cases, the research conclusions fail to provide useful insights or solutions to the problem, or the report writer fails to offer specific suggestions for translating the research findings into management strategy. These pitfalls can be avoided by paying due attention to the research objectives throughout the project and allocating sufficient time and resources to do a good job interpreting research results for those who will need to act on them.

Applied Example: Bookends’ New Customer Campaign

Your research findings and recommendations identified three segments for Bookends to focus on. Based on the demographics, lifestyle, and spending patterns found during your marketing research, you’re able to name them: 1) Bored Empty-Nesters, 2) Busy Families, and 3) Hipster Wannabes. Dan has a decent-sized clientele across all three groups, and they are pretty good spenders when they come in. But until now he hasn’t done much to purposely attract any of them.

With newly identified segments in focus, you and Dan begin brainstorming about a marketing mix to target each group. What types of books and other products would appeal to each one? What activities or events would bring them into the store? Are there promotions or particular messages that would induce them to buy at Bookends instead of Amazon or another bookseller? How will Dan reach and communicate with each group? And what can you do to bring more new customers into the store within these target groups?

Even though Bookends is a real-life project with serious consequences for your uncle Dan, it’s also a fun laboratory where you can test out some of the principles you’re learning in your marketing class. You’re figuring out quickly what it’s like to be a marketer.

Well done, rookie!

Check Your Understanding

Answer the question(s) below to see how well you understand the topics covered in this outcome. This short quiz does  not  count toward your grade in the class, and you can retake it an unlimited number of times.

Use this quiz to check your understanding and decide whether to (1) study the previous section further or (2) move on to the next section.

• Revision and Adaptation. Authored by : Lumen Learning. License : CC BY: Attribution
• Chapter 3: Marketing Research: An Aid to Decision Making, from Introducing Marketing. Authored by : John Burnett. Provided by : Global Text. Located at : http://solr.bccampus.ca:8001/bcc/file/ddbe3343-9796-4801-a0cb-7af7b02e3191/1/Core%20Concepts%20of%20Marketing.pdf . License : CC BY: Attribution
• Urban life (Version 2.0). Authored by : Ian D. Keating. Located at : https://www.flickr.com/photos/ian-arlett/19313315520/ . License : CC BY: Attribution

What is the importance of hypothesis in research?

A hypothesis forms the base of research that defines the relationship between two or more research variables. The main topic of discussion is defined based on the hypothesis statement that is clear and concise.

Basic characteristics of a hypothesis:

• It is self-explanatory and consistent.
• It does not contradict itself.
• It is testable and clearly stated.
• It defines the relationship between research variables.
• It contains facts and evidence to support the author’s claims.

A research hypothesis talks about the possible outcome or result of the experiment. The key concepts and the connection between the statement of problem and literature review are defined using the hypothesis.

It is written in a declarative form, which makes it brief and to the point. Formulate a hypothesis in a testable form so that your readers can understand the core concept of the research.

Steps involved in writing a research paper:

Formulation of question : A research question is clear and concise, and it focuses on the area of concern. It states the problem that the author is trying to solve through the research.

Background research : Analyze research papers published in your field of study. Provide context by sharing your ideas and observations on the related research theories and scientific concepts.

Create a hypothesis : State the expected outcome and define the research variables and establish a link between them.

Reporting data : Use tables and graphs to represent the data by referring to the images and charts correctly. Determine whether you want to use the decimal system or alphanumeric system.

Research findings : Write a result overview and report the research findings. Try to be as specific as possible while summarizing the results.

Before writing the introduction section, ensure that your hypothesis is based on the main topic of research. Double-check the independent and dependent variables and ensure that it can be tested.

What are the different types of research hypothesis?

Research Hypothesis : The research hypothesis also known as the non-parametric hypothesis; it states the nature of the relationship between the research variables. The variables identify possible solutions to the main problem statement.  As the data is not represented in quantitative form, the research hypothesis is not tested by statistical methods.

Null hypothesis : This hypothesis simply establishes that no statistical importance in a set of data exists. It proves that no variation exists between two or more variables.

Alternative hypothesis : This hypothesis is the opposite of the null hypothesis. If the null hypothesis is rejected, then you can use the alternative hypothesis to complete the experiment.

Various functions of the research hypothesis:

• To test theories and concepts
• To confirm or disconfirm theories
• To identify the right methodology or tools
• To provide a general framework
• To discover new prospects of the research topic

Hypothesis testing:

State the hypothesis : Identify the hypothesis that needs to be tested? Which type of hypothesis are you working on? Is it an alternative hypothesis or null hypothesis?

Plan : How do you plan to utilize the sample data? What methods are you going to use for testing the hypothesis?

Analyze : Perform sample analysis to test your hypothesis. Compare two different sets of data you have received before and after the sample selection.

Acceptance/Rejection : Evaluate the results by subjecting the hypothesis to tests. You can determine whether or not the hypothesis is valid based on the test results.

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Marketing91

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?

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

About Hitesh Bhasin

Hi, I am an MBA and the CEO of Marketing91. I am a Digital Marketer and an Entrepreneur with 12 Years of experience in Business and Marketing. Business is my passion and i have established myself in multiple industries with a focus on sustainable growth. You will generally find me online at the Marketing91 Academy .

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• Qualitative Research: Meaning, and Features of Qualitative Research
• Research Ethics – Importance and Principles of Ethics in Research
• What is a Vision Statement? 10 Examples to write your own
• Technical Specifications: Importance & How to write Technical Specification
• What is a Design Brief and How to Write it in 9 Easy Steps?
• Press Release: Importance & How to Write a Press Release

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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Shipping Scale Market Global Competition and Business Outlook 2023

The Shipping Scale report is the most important research for who looks for complete information on the Shipping Scale market. The report covers all information on the global and regional markets including historic and future trends for market demand, size, trading, supply, competitors, and prices as well as global predominant vendor's information. The forecast market information, SWOT analysis, Shipping Scale market scenario, and feasibility study are the vital aspects analyzed in this report.

Top Leading Companies of Global Shipping Scale Market are Mettler-Toledo International Inc., Avery Weigh-Tronix, Fairbanks Scales, OHAUS Corporation, Rice Lake Weighing Systems, Adam Equipment, Dibal, Bizerba, CAS Corporation, Cardinal Scale Manufacturing Company, Ishida Co., Ltd., Precia Molen, Minebea Intec, RADWAG Balances and Scales, A&D Company, Limited, PCE Instruments,

Get a free Sample Copy of this Report:

https://www.marketinsightsreports.com/reports/092813019703/global-and-united-states-shipping-scale-market-report-forecast-2023-2029/inquiry?mode=70

Market Overview :

By Application:

Regional Analysis for Shipping Scale Market:

North American Market (USA, Canada, North America, Mexico), European Market (Germany, France, UK, Russia, Italy), Asia Pacific Market (China, Japan, South Korea, Asian Countries, India, Southeast Asia), South American Market (Brazil, Argentina ) , Colombia, etc.), Middle East and Africa Market (Saudi Peninsula, UAE, Egypt, Nigeria, South Africa)

Global Shipping Scale Market provides valuable insights such as:

-Nature of the competition in Global Shipping Scale Market

-Key segments with largest share in Global Shipping Scale Market

-Emerging technologies that can pave way for product innovation

-Consumer purchasing trends related to the products and services in Global Shipping Scale Market

-End-use industries expected to foster growth in the market

-Impact of COVID-19 pandemic on manufacturing and production cycles in the Shipping Scale Market

-Region-specific policy frameworks and regulatory guidelines

-Unexplored geographical regions with lucrative opportunities for stakeholders in the market

Future of Shipping Scale Market - Driving Factors and Hindering Challenges

The future of the Shipping Scale market is one that is ripe with potential and growth opportunities. The primary driving factors behind the growth of the market include advancements in technology, increasing consumer demand, and favorable government regulations. With technology making it possible to create new and innovative products and services, the Shipping Scale market is set to experience exponential growth in the coming years. Additionally, consumers are becoming more aware of the benefits of Shipping Scale and are demanding more high-quality products and services, further fueling the growth of the market. This research report has been accumulated based on static and dynamic views of the businesses.

Crucial Elements from the Table of Contents of Global Shipping Scale Market :

Chapter 1: Shipping Scale Market Overview Chapter 2: Global Shipping Scale Market Competition, Profiles/Analysis, Strategies Chapter 3: Global Shipping Scale Capacity, Production, Revenue (Value) by Region (2016-2021) Chapter 4: Global Shipping Scale Supply (Production), Consumption, Export, Import by Region (2016-2021) Chapter 5: Global Shipping Scale Market Regional Highlights Chapter 6: Industrial Chain, Sourcing Strategy, and Downstream Buyers Chapter 7: Marketing Strategy Analysis, Distributors/Traders Chapter 8: Market Effect Factors Analysis Chapter 9: Market Decisions for the present scenario Chapter 10: Global Shipping Scale Market Forecast (2023-2029) Chapter 11: Case Studies Chapter 12: Research Findings and Conclusion

Explore Full Report With Detailed TOC Here :

https://www.marketinsightsreports.com/reports/092813019703/global-and-united-states-shipping-scale-market-report-forecast-2023-2029?mode=70

Key questions answered in the report:

• What is the growth potential of the Shipping Scale market?
• What growth opportunities might arise in the Shipping Scale industry in the years to come?
• What are the most significant challenges that the Shipping Scale market could face in the future?
• What are the key technologies and Shipping Scale Market trends shaping the Shipping Scale Market?
• What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Shipping Scale Industry?

Key Market Focuses

• Does this report consider the impact of COVID-19 and the Russia-Ukraine war on the Shipping Scale market?

Yes. As the COVID-19 and the Russia-Ukraine war are profoundly affecting the global supply chain relationship and price system, we have definitely taken them into consideration throughout the research, we elaborate at full length on the impact of the pandemic and the war on the Shipping Scale Industry.

• How do you determine the list of the key players included in the report?

With the aim of clearly revealing the competitive situation of the industry, we concretely analyze not only the leading enterprises that have a voice on a global scale, but also the regional small and medium-sized companies that play key roles and have plenty of potential growth.

• What are your main data sources?

Both Primary and Secondary data sources are being used while compiling the report.

Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users.

Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.

• Can I modify the scope of the report and customize it to suit my requirements?

Yes. Customized requirements of multi-dimensional, deep-level and high-quality can help our customers precisely grasp market opportunities, effortlessly confront market challenges, properly formulate market strategies and act promptly, thus to win them sufficient time and space for market competition.

Reasons to Purchase this Report -

- Analyzing the outlook of the market with the recent trends and SWOT analysis.

- Market dynamics scenario, along with growth opportunities of the market in the years to come.

- Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects.

- Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.

- Market value (USD Million) and volume (Units Million) data for each segment and sub-segment

- Competitive landscape involving the market share of major players, along with the new projects and -strategies adopted by players in the past years.

SPECIAL OFFER: AVAIL UP TO 25% DISCOUNT ON THIS REPORT:

Finally, the Shipping Scale Market report is the believable source for gaining the market research that will exponentially accelerate your business. The report gives the principle locale, economic situations with the item value, benefit, limit, generation, supply, request, and market development rate and figure, and so on. The Shipping Scale industry report additionally presents a new task SWOT examination, speculation attainability investigation, and venture return investigation.

Following are a few examples of the customisation requests:

The Shipping Scale market is a diverse and constantly evolving industry, with a wide range of customization requests from clients. Here are a few examples of the most common customization requests:

Personalization of products : Many clients in the Shipping Scale market request custom-made products that cater to their specific needs and preferences. This can include everything from customized packaging and branding to unique product features and sizes.

Customized packaging : Packaging is a key component of any product, and clients in the Shipping Scale market often request customized packaging that reflects their brand identity and aesthetic. This can include custom labels, stickers, and even custom boxes with unique shapes and designs.

Custom product formulations : Clients in the Shipping Scale market often request custom product formulations that are tailored to their specific needs.

Exclusive collaborations : Many brands in the Shipping Scale market collaborate with influencers or celebrities to create limited-edition products or collections. These collaborations often feature exclusive packaging, unique product formulations, and special branding.

Additional paid Services :

• Client will get one free update on the purchase of Corporate User License.
• Quarterly Industry Update for 1 Year at 40% of the report cost per update.
• One dedicated research analyst allocated to the client.
• Fast Query resolution within 48 hours.
• Industry Newsletter at USD 100 per month per issue.

Contact Us :

Irfan Tamboli (Head of Sales) Market Insights Reports Phone: + 1704 266 3234 | +91-750-707-8687 s[email protected] | [email protected]

COMTEX_441130253/2599/2023-09-28T07:47:37

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US government shutdown: What is it and who would be affected?

Sept 28 (Reuters) - U.S. government services would be disrupted and hundreds of thousands of federal workers would be furloughed without pay if Congress fails to provide funding for the fiscal year starting Oct. 1. Workers deemed essential would remain on the job, but without pay.

Here is a guide to what would stay open and what would shut down, according to agency shutdown plans :

The 2 million U.S. military personnel would remain at their posts, but roughly half of the Pentagon's 800,000 civilian employees would be furloughed.

Contracts awarded before the shutdown would continue, and the Pentagon could place new orders for supplies or services needed to protect national security. Other new contracts, including renewals or extensions, would not be awarded . Payments to defense contractors such as Boeing (BA.N) , Lockheed Martin (LMT.N) and RTX (RTX.N) , formerly known as Raytheon, could be delayed.

The Department of Energy's National Nuclear Security Administration would continue maintaining nuclear weapons.

LAW ENFORCEMENT

Agents at the FBI, the Drug Enforcement Administration (DEA) and other federal law enforcement agencies would remain on the job , and prison staffers would continue to work.

The Secret Service and the Coast Guard would also continue operations, and most employees would continue to work.

Most of the Federal Trade Commission's consumer-protection workers would be furloughed, as would half of its antitrust employees.

SOCIAL SECURITY, MEDICARE AND OTHER BENEFITS

The Social Security Administration would continue to issue retirement and disability benefits, though the agency might have to delay its announcement of its annual increase in payments. read more

BORDERS AND HOMELAND SECURITY

Most Border Patrol and immigration enforcement agents would continue to work, as would most customs officers.

Local governments would not get new aid to shelter migrants.

The Cybersecurity and Infrastructure Security Agency would suspend security reviews that help schools, local governments and other institutions defend against ransomware.

FEDERAL COURTS

Federal courts have enough money to stay open until at least Oct. 13 . Activities might be scaled back after that point. The Supreme Court would stay open as well.

Criminal prosecutions, including the two federal cases against former President Donald Trump , would continue. Most civil litigation would be postponed. The government's landmark Google antitrust lawsuit would continue.

Lawmakers continue to collect paychecks, even as other federal workers do not. Staffers do not get paid, though those deemed essential would be required to work.

TRANSPORTATION

Airport security screeners and air-traffic control workers would be required to work, according to recent contingency plans, though absenteeism could be a problem. Some airports had to suspend operations during a shutdown in 2019 when traffic controllers called in sick.

Training for 1,000 new air-traffic controllers would stop, leaving the system understaffed. The Transportation Security Administration would not be able to hire new airport security screeners ahead of the busy holiday travel season.

Some major infrastructure projects could face delays as environmental reviews and permitting would be disrupted, according to the White House.

FOREIGN AFFAIRS

U.S. embassies and consulates would remain open. Passport and visa processing would continue as long as there were sufficient fees to cover operations. Nonessential official travel, speeches and other events would be curtailed.

Some foreign aid programs could run out of money as well.

NATIONAL PARKS AND NATURAL RESOURCES

It's not clear how national parks, national monuments and other sites would be affected. Many remained open during a 2018-2019 shutdown, through restrooms and information desks were closed and waste disposal was halted. They were closed during a 2013 shutdown.

Wildfire fighting efforts would continue, according to the Agriculture Department's 2020 contingency plan, though timber sales on national forest lands would be curtailed and fewer recreation permits would be issued.

The Smithsonian museums that line the National Mall would close, as would the National Zoo. That would bring a premature end to the Zoo's farewell celebration for its three giant pandas, which are due to return to China.

Scientific research would be disrupted as agencies like the National Institutes of Health, the National Science Foundation and the National Oceanographic and Atmospheric Administration (NOAA) would furlough most of their workers, according to recent contingency plans.

The National Aeronautics and Space Administration (NASA) would continue to support the International Space Station and track satellites, but 17,000 of its 18,300 employees would be furloughed.

Weather forecasts and fisheries regulation would continue, as would patent and trademark reviews. Tests of new drugs and medical devices would continue.

The Federal Communications Commission (FCC) would suspend consumer-protection activities, equipment reviews, and licensing of TV and radio stations. It would continue to distribute telecommunications subsidies and its broadband mapping effort.

The Centers for Disease Control and Prevention (CDC) would continue to monitor disease outbreaks, though other public health activities could suffer as more than half of the agency's workers would be furloughed.

The National Institutes of Health would furlough most of its staff and delay new clinical trials for medical treatments.

[1/5] A general view of the U.S. Capitol, where Congress will return Tuesday to deal with a series of spending bills before funding runs out and triggers a partial U.S. government shutdown, in Washington, U.S. September 25, 2023. REUTERS/Jonathan Ernst Acquire Licensing Rights

Healthcare services for military veterans and Native Americans would continue.

Most inspections of hazardous waste sites and drinking water and chemical facilities would stop.

Food-safety inspections by the Food and Drug Administration (FDA) could be delayed.

FINANCIAL REGULATION

The Securities and Exchange Commission (SEC) would furlough roughly 90% of its 4,600 employees and suspend most activities, leaving only a skeleton staff to respond to emergencies.

Likewise, the Commodities and Futures Trading Commission (CFTC) would furlough almost all of its employees and cease oversight, enforcement and regulation, according to its 2021 plan.

The Federal Reserve, the Federal Deposit Insurance Corporation (FDIC) and the Office of the Comptroller of the Currency would continue as normal, as they are funded by industry fees rather than congressional appropriations.

The Financial Industry Regulatory Authority (FINRA), an industry-financed brokerage oversight body, would continue to operate.

ECONOMIC DATA

The publication of major U.S. economic data, including employment and inflation reports of critical importance to policymakers and investors, would be suspended , according to the Biden administration.

The Social Security Administration would continue to issue retirement and disability benefits, and payments would continue under the Medicare and Medicaid healthcare programs.

Military veterans' benefits would also continue, according to a 2021 contingency plan.

Nutrition benefits provided to 7 million mothers through the Women, Infants and Children program would be cut within days, according to Agriculture Secretary Tom Vilsack.

Food aid through the Supplemental Nutrition Assistance Program (SNAP) would go out as normal for October, but could be affected after that, he said.

TAX COLLECTION

The Internal Revenue Service (IRS) has not released a current contingency plan. The Committee for a Responsible Federal Budget, a watchdog group, says the agency would operate as normal and all 83,000 employees would continue to be paid because its funding would not expire.

DISASTER RESPONSE

The Federal Emergency Management Agency (FEMA) would be at risk of running out of disaster-relief funds. The agency is already delaying payments to some long-term recovery projects in order to keep money on hand for more immediate relief during hurricane and wildfire season.

Pell Grants and student loans would continue to be paid, but could be disrupted as most Education Department employees would be furloughed, according to the agency's 2021 plan.

A protracted shutdown could "severely curtail" aid to schools, universities and other educational institutions, the department says. It also could delay funds that are due to be awarded later in the year.

The U.S. Army Corps of Engineers would continue to operate locks, dams and flood control facilities. Most employees would not be furloughed.

According to the White House, 10,000 children from low-income families would lose access to the Head Start preschool program.

SMALL BUSINESS SUPPORT

The Small Business Administration would not be able to issue any new loans, though loans for businesses hurt by natural disasters would continue.

AGRICULTURE

Meat and egg inspections would continue, but some lab services would be disrupted, making it harder to fight animal diseases. Crop insurance would not be affected, but some loan programs would be. Research, conservation and rural development programs would be shut down.

Workplace safety inspections would be limited, and investigations into unfair pay practices would be suspended, according to the White House.

The ability of the National Labor Relations Board (NLRB) to mediate labor disputes would be curtailed because almost all of its 1,200 employees would be furloughed, according to a 2022 plan.

Monthly subsidies for public housing and low-income housing aid would be at risk. The Federal Housing Administration would continue to back insured mortgages, and Ginnie Mae would continue to back the secondary mortgage market. New homebuyers in rural areas would not be able to get loans from the Agriculture Department.

WHITE HOUSE

In the 2018-2019 shutdown, the White House furloughed 1,100 of 1,800 staff in the Executive Office of the President. Some offices, such as the National Security Council, continued at full strength, while others like the Office of Management and Budget (OMB) were scaled back sharply.

White House furloughs could make it harder for Republicans in the House of Representatives to get information for their impeachment investigation of Democratic President Joe Biden.

The U.S. Constitution specifies that the president continues to get paid.

MAIL DELIVERY

The U.S. Postal Service would be unaffected, as it does not depend on Congress for funding.

Reporting by Andy Sullivan, Pete Schroeder, Howard Schneider, Moira Warburton, Nate Raymond, Makini Brice, Steve Holland, Julia Harte, Diane Bartz, Andrew Chung and Lucia Mutikani; Editing by Scott Malone, Jonathan Oatis and Aurora Ellis

Our Standards: The Thomson Reuters Trust Principles.

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Beijing and Washington are paving the way for Chinese President Xi Jinping to visit the U.S., the Wall Street Journal reported on Thursday.

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• Experience Management

Market Research

Market research definition

Market research – in-house or outsourced, market research in the age of data, when to use market research.

• Types of market research 

Different types of primary research

How to do market research (primary data), how to do secondary market research, communicating your market research findings, choose the right platform for your market research, try qualtrics for free, the ultimate guide to market research: how to conduct it like a pro.

27 min read Wondering how to do market research? Or even where to start learning about it? Use our ultimate guide to understand the basics and discover how you can use market research to help your business.

Market research is the practice of gathering information about the needs and preferences of your target audience – potential consumers of your product.

When you understand how your target consumer feels and behaves, you can then take steps to meet their needs and mitigate the risk of an experience gap – where there is a shortfall between what a consumer expects you to deliver and what you actually deliver. Market research can also help you keep abreast of what your competitors are offering, which in turn will affect what your customers expect from you.

Market research connects with every aspect of a business – including brand , product , customer service , marketing and sales.

Market research generally focuses on understanding:

• The consumer (current customers, past customers, non-customers, influencers))
• The company (product or service design, promotion, pricing, placement, service, sales)
• The competitors (and how their market offerings interact in the market environment)
• The industry overall (whether it’s growing or moving in a certain direction)

Watch a limited release of the X4 Summit 2023 sessions on demand

Why is market research important?

A successful business relies on understanding what like, what they dislike, what they need and what messaging they will respond to. Businesses also need to understand their competition to identify opportunities to differentiate their products and services from other companies.

Today’s business leaders face an endless stream of decisions around target markets, pricing, promotion, distribution channels, and product features and benefits . They must account for all the factors involved, and there are market research studies and methodologies strategically designed to capture meaningful data to inform every choice. It can be a daunting task.

Market research allows companies to make data-driven decisions to drive growth and innovation.

What happens when you don’t do market research?

Without market research, business decisions are based at best on past consumer behavior, economic indicators, or at worst, on gut feel. Decisions are made in a bubble without thought to what the competition is doing. An important aim of market research is to remove subjective opinions when making business decisions. As a brand you are there to serve your customers, not personal preferences within the company. You are far more likely to be successful if you know the difference, and market research will help make sure your decisions are insight-driven.

Traditionally there have been specialist market researchers who are very good at what they do, and businesses have been reliant on their ability to do it. Market research specialists will always be an important part of the industry, as most brands are limited by their internal capacity, expertise and budgets and need to outsource at least some aspects of the work.

However, the market research external agency model has meant that brands struggled to keep up with the pace of change. Their customers would suffer because their needs were not being wholly met with point-in-time market research.

Businesses looking to conduct market research have to tackle many questions –

• Who are my consumers, and how should I segment and prioritize them?
• What are they looking for within my category?
• How much are they buying, and what are their purchase triggers, barriers, and buying habits?
• Will my marketing and communications efforts resonate?
• Is my brand healthy ?
• What product features matter most?
• Is my product or service ready for launch?
• Are my pricing and packaging plans optimized?

They all need to be answered, but many businesses have found the process of data collection daunting, time-consuming and expensive. The hardest battle is often knowing where to begin and short-term demands have often taken priority over longer-term projects that require patience to offer return on investment.

Today however, the industry is making huge strides, driven by quickening product cycles, tighter competition and business imperatives around more data-driven decision making. With the emergence of simple, easy to use tools , some degree of in-house market research is now seen as essential, with fewer excuses not to use data to inform your decisions. With greater accessibility to such software, everyone can be an expert regardless of level or experience.

How is this possible?

The art of research hasn’t gone away. It is still a complex job and the volume of data that needs to be analyzed is huge. However with the right tools and support, sophisticated research can look very simple – allowing you to focus on taking action on what matters.

If you’re not yet using technology to augment your in-house market research, now is the time to start.

The most successful brands rely on multiple sources of data to inform their strategy and decision making, from their marketing segmentation to the product features they develop to comments on social media. In fact, there’s tools out there that use machine learning and AI to automate the tracking of what’s people are saying about your brand across all sites.

The emergence of newer and more sophisticated tools and platforms gives brands access to more data sources than ever and how the data is analyzed and used to make decisions. This also increases the speed at which they operate, with minimal lead time allowing brands to be responsive to business conditions and take an agile approach to improvements and opportunities.

Expert partners have an important role in getting the best data, particularly giving access to additional market research know-how, helping you find respondents , fielding surveys and reporting on results.

How do you measure success?

Business activities are usually measured on how well they deliver return on investment (ROI). Since market research doesn’t generate any revenue directly, its success has to be measured by looking at the positive outcomes it drives – happier customers, a healthier brand, and so on.

When changes to your products or your marketing strategy are made as a result of your market research findings, you can compare on a before-and-after basis to see if the knowledge you acted on has delivered value.

Regardless of the function you work within, understanding the consumer is the goal of any market research. To do this, we have to understand what their needs are in order to effectively meet them. If we do that, we are more likely to drive customer satisfaction , and in turn, increase customer retention .

Several metrics and KPIs are used to gauge the success of decisions made from market research results, including

• Brand awareness within the target market
• Share of wallet
• CSAT (customer satisfaction)
• NPS (Net Promoter Score)

You can use market research for almost anything related to your current customers, potential customer base or target market. If you want to find something out from your target audience, it’s likely market research is the answer.

Here are a few of the most common uses:

Buyer segmentation and profiling

Segmentation is a popular technique that separates your target market according to key characteristics, such as behavior, demographic information and social attitudes. Segmentation allows you to create relevant content for your different segments, ideally helping you to better connect with all of them.

Buyer personas are profiles of fictional customers – with real attributes. Buyer personas help you develop products and communications that are right for your different audiences, and can also guide your decision-making process. Buyer personas capture the key characteristics of your customer segments, along with meaningful insights about what they want or need from you. They provide a powerful reminder of consumer attitudes when developing a product or service, a marketing campaign or a new brand direction.

By understanding your buyers and potential customers, including their motivations, needs, and pain points, you can optimize everything from your marketing communications to your products to make sure the right people get the relevant content, at the right time, and via the right channel .

Attitudes and Usage surveys

Attitude & Usage research helps you to grow your brand by providing a detailed understanding of consumers. It helps you understand how consumers use certain products and why, what their needs are, what their preferences are, and what their pain points are. It helps you to find gaps in the market, anticipate future category needs, identify barriers to entry and build accurate go-to-market strategies and business plans.

Marketing strategy

Effective market research is a crucial tool for developing an effective marketing strategy – a company’s plan for how they will promote their products.

It helps marketers look like rock stars by helping them understand the target market to avoid mistakes, stay on message, and predict customer needs . It’s marketing’s job to leverage relevant data to reach the best possible solution  based on the research available. Then, they can implement the solution, modify the solution, and successfully deliver that solution to the market.

Product development

You can conduct market research into how a select group of consumers use and perceive your product – from how they use it through to what they like and dislike about it. Evaluating your strengths and weaknesses early on allows you to focus resources on ideas with the most potential and to gear your product or service design to a specific market.

Chobani’s yogurt pouches are a product optimized through great market research . Using product concept testing – a form of market research – Chobani identified that packaging could negatively impact consumer purchase decisions. The brand made a subtle change, ensuring the item satisfied the needs of consumers. This ability to constantly refine its products for customer needs and preferences has helped Chobani become Australia’s #1 yogurt brand and increase market share.

Pricing decisions

Market research provides businesses with insights to guide pricing decisions too. One of the most powerful tools available to market researchers is conjoint analysis, a form of market research study that uses choice modeling to help brands identify the perfect set of features and price for customers. Another useful tool is the Gabor-Granger method, which helps you identify the highest price consumers are willing to pay for a given product or service.

Brand tracking studies

A company’s brand is one of its most important assets. But unlike other metrics like product sales, it’s not a tangible measure you can simply pull from your system. Regular market research that tracks consumer perceptions of your brand allows you to monitor and optimize your brand strategy in real time, then respond to consumer feedback to help maintain or build your brand with your target customers.

Advertising and communications testing

Advertising campaigns can be expensive, and without pre-testing, they carry risk of falling flat with your target audience. By testing your campaigns, whether it’s the message or the creative, you can understand how consumers respond to your communications before you deploy them so you can make changes in response to consumer feedback before you go live.

Finder, which is one of the world’s fastest-growing online comparison websites, is an example of a brand using market research to inject some analytical rigor into the business . Fueled by great market research, the business lifted brand awareness by 23 percent, boosted NPS by 8 points, and scored record profits – all within 10 weeks.

Competitive analysis

Another key part of developing the right product and communications is understanding your main competitors and how consumers perceive them. You may have looked at their websites and tried out their product or service, but unless you know how consumers perceive them, you won’t have an accurate view of where you stack up in comparison. Understanding their position in the market allows you to identify the strengths you can exploit, as well as any weaknesses you can address to help you compete better.

Customer Story

See How Yamaha Does Product Research

Types of market research

Although there are many types market research, all methods can be sorted into one of two categories: primary and secondary.

Primary research

Primary research is market research data that you collect yourself. This is raw data collected through a range of different means – surveys , focus groups,  , observation and interviews being among the most popular.

Primary information is fresh, unused data, giving you a perspective that is current or perhaps extra confidence when confirming hypotheses you already had. It can also be very targeted to your exact needs. Primary information can be extremely valuable. Tools for collecting primary information are increasingly sophisticated and the market is growing rapidly.

Historically, conducting market research in-house has been a daunting concept for brands because they don’t quite know where to begin, or how to handle vast volumes of data. Now, the emergence of technology has meant that brands have access to simple, easy to use tools to help with exactly that problem. As a result, brands are more confident about their own projects and data with the added benefit of seeing the insights emerge in real-time.

Secondary research

Secondary research is the use of data that has already been collected, analyzed and published – typically it’s data you don’t own and that hasn’t been conducted with your business specifically in mind, although there are forms of internal secondary data like old reports or figures from past financial years that come from within your business. Secondary research can be used to support the use of primary research.

Secondary research can be beneficial to small businesses because it is sometimes easier to obtain, often through research companies. Although the rise of primary research tools are challenging this trend by allowing businesses to conduct their own market research more cheaply, secondary research is often a cheaper alternative for businesses who need to spend money carefully. Some forms of secondary research have been described as ‘lean market research’ because they are fast and pragmatic, building on what’s already there.

Because it’s not specific to your business, secondary research may be less relevant, and you’ll need to be careful to make sure it applies to your exact research question. It may also not be owned, which means your competitors and other parties also have access to it.

Primary or secondary research – which to choose?

Both primary and secondary research have their advantages, but they are often best used when paired together, giving you the confidence to act knowing that the hypothesis you have is robust.

Secondary research is sometimes preferred because there is a misunderstanding of the feasibility of primary research. Thanks to advances in technology, brands have far greater accessibility to primary research, but this isn’t always known.

If you’ve decided to gather your own primary information, there are many different data collection methods that you may consider. For example:

• Customer surveys
• Focus groups
• Observation

Think carefully about what you’re trying to accomplish before picking the data collection method(s) you’re going to use. Each one has its pros and cons. Asking someone a simple, multiple-choice survey question will generate a different type of data than you might obtain with an in-depth interview. Determine if your primary research is exploratory or specific, and if you’ll need qualitative research, quantitative research, or both.

Qualitative vs quantitative

Another way of categorizing different types of market research is according to whether they are qualitative or quantitative.

Qualitative research

Qualitative research is the collection of data that is non-numerical in nature. It summarizes and infers, rather than pin-points an exact truth. It is exploratory and can lead to the generation of a hypothesis.

Market research techniques that would gather qualitative data include:

• Interviews (face to face / telephone)
• Open-ended survey questions

Researchers use these types of market research technique because they can add more depth to the data. So for example, in focus groups or interviews, rather than being limited to ‘yes’ or ‘no’ for a certain question, you can start to understand why someone might feel a certain way.

Quantitative research

Quantitative research is the collection of data that is numerical in nature. It is much more black and white in comparison to qualitative data, although you need to make sure there is a representative sample if you want the results to be reflective of reality.

Quantitative researchers often start with a hypothesis and then collect data which can be used to determine whether empirical evidence to support that hypothesis exists.

Quantitative research methods include:

• Questionnaires
• Review scores

Exploratory and specific research

Exploratory research is the approach to take if you don’t know what you don’t know. It can give you broad insights about your customers, product, brand, and market. If you want to answer a specific question, then you’ll be conducting specific research.

• Exploratory . This research is general and open-ended, and typically involves lengthy interviews with an individual or small focus group.
• Specific . This research is often used to solve a problem identified in exploratory research. It involves more structured, formal interviews.

Exploratory primary research is generally conducted by collecting qualitative data. Specific research usually finds its insights through quantitative data.

Primary research can be qualitative or quantitative, large-scale or focused and specific. You’ll carry it out using methods like surveys – which can be used for both qualitative and quantitative studies – focus groups, observation of consumer behavior, interviews, or online tools.

Step 1: Identify your research topic

Research topics could include:

• Product features
• Product or service launch
• Understanding a new target audience (or updating an existing audience)
• Brand identity
• Marketing campaign concepts
• Customer experience

Step 2: Draft a research hypothesis

A hypothesis is the assumption you’re starting out with. Since you can disprove a negative much more easily than prove a positive, a hypothesis is a negative statement such as ‘price has no effect on brand perception’.

Step 3: Determine which research methods are most effective

Your choice of methods depends on budget, time constraints, and the type of question you’re trying to answer. You could combine surveys, interviews and focus groups to get a mix of qualitative and quantitative data.

Step 4: Determine how you will collect and analyze your data.

Primary research can generate a huge amount of data, and when the goal is to uncover actionable insight, it can be difficult to know where to begin or what to pay attention to.

The rise in brands taking their market research and data analysis in-house has coincided with the rise of technology simplifying the process. These tools pull through large volumes of data and outline significant information that will help you make the most important decisions.

Step 5: Conduct your research!

This is how you can run your research using Qualtrics CoreXM

• Pre-launch – Here you want to ensure that the survey/ other research methods conform to the project specifications (what you want to achieve/research)
• Soft launch – Collect a small fraction of the total data before you fully launch. This means you can check that everything is working as it should and you can correct any data quality issues.
• Full launch – You’ve done the hard work to get to this point. If you’re using a tool, you can sit back and relax, or if you get curious you can check on the data in your account.
• Review – review your data for any issues or low-quality responses. You may need to remove this in order not to impact the analysis of the data.

A helping hand

If you are missing the skills, capacity or inclination to manage your research internally, Qualtrics Research Services can help. From design, to writing the survey based on your needs, to help with survey programming, to handling the reporting, Research Services acts as an extension of the team and can help wherever necessary.

Secondary market research can be taken from a variety of places. Some data is completely free to access – other information could end up costing hundreds of thousands of dollars. There are three broad categories of secondary research sources:

• Public sources – these sources are accessible to anyone who asks for them. They include census data, market statistics, library catalogs, university libraries and more. Other organizations may also put out free data from time to time with the goal of advancing a cause, or catching people’s attention.
• Internal sources – sometimes the most valuable sources of data already exist somewhere within your organization. Internal sources can be preferable for secondary research on account of their price (free) and unique findings. Since internal sources are not accessible by competitors, using them can provide a distinct competitive advantage.
• Commercial sources – if you have money for it, the easiest way to acquire secondary market research is to simply buy it from private companies. Many organizations exist for the sole purpose of doing market research and can provide reliable, in-depth, industry-specific reports.

No matter where your research is coming from, it is important to ensure that the source is reputable and reliable so you can be confident in the conclusions you draw from it.

How do you know if a source is reliable?

Use established and well-known research publishers, such as the XM Institute , Forrester and McKinsey . Government websites also publish research and this is free of charge. By taking the information directly from the source (rather than a third party) you are minimizing the risk of the data being misinterpreted and the message or insights being acted on out of context.

How to apply secondary research

The purpose and application of secondary research will vary depending on your circumstances. Often, secondary research is used to support primary research and therefore give you greater confidence in your conclusions. However, there may be circumstances that prevent this – such as the timeframe and budget of the project.

Keep an open mind when collecting all the relevant research so that there isn’t any collection bias. Then begin analyzing the conclusions formed to see if any trends start to appear. This will help you to draw a consensus from the secondary research overall.

Market research success is defined by the impact it has on your business’s success. Make sure it’s not discarded or ignored by communicating your findings effectively. Here are some tips on how to do it.

• Less is more – Preface your market research report with executive summaries that highlight your key discoveries and their implications
• Lead with the basic information – Share the top 4-5 recommendations in bullet-point form, rather than requiring your readers to go through pages of analysis and data
• Model the impact – Provide examples and model the impact of any changes you put in place based on your findings
• Show, don’t tell – Add illustrative examples that relate directly to the research findings and emphasize specific points
• Speed is of the essence – Make data available in real-time so it can be rapidly incorporated into strategies and acted upon to maximize value
• Work with experts – Make sure you’ve access to a dedicated team of experts ready to help you design and launch successful projects

Trusted by 8,500 brands for everything from product testing to competitor analysis, DesignXM is the world’s most powerful and flexible research platform . With over 100 question types and advanced logic, you can build out your surveys and see real-time data you can share across the organization. Plus, you’ll be able to turn data into insights with iQ, our predictive intelligence engine that runs complicated analysis at the click of a button.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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