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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 the 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 that 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.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

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.

Learn 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.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

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“A fact is a simple statement that everyone believes. It is innocent, unless found guilty. A hypothesis is a novel suggestion that no one wants to believe. It is guilty until found effective.”

– Edward Teller, Nuclear Physicist

During my first brainstorming meeting on my first project at McKinsey, this very serious partner, who had a PhD in Physics, looked at me and said, “So, Joe, what are your main hypotheses.” I looked back at him, perplexed, and said, “Ummm, my what?” I was used to people simply asking, “what are your best ideas, opinions, thoughts, etc.” Over time, I began to understand the importance of hypotheses and how it plays an important role in McKinsey’s problem solving of separating ideas and opinions from facts.

What is a Hypothesis?

“Hypothesis” is probably one of the top 5 words used by McKinsey consultants. And, being hypothesis-driven was required to have any success at McKinsey. A hypothesis is an idea or theory, often based on limited data, which is typically the beginning of a thread of further investigation to prove, disprove or improve the hypothesis through facts and empirical data.

The first step in being hypothesis-driven is to focus on the highest potential ideas and theories of how to solve a problem or realize an opportunity.

Let’s go over an example of being hypothesis-driven.

Let’s say you own a website, and you brainstorm ten ideas to improve web traffic, but you don’t have the budget to execute all ten ideas. The first step in being hypothesis-driven is to prioritize the ten ideas based on how much impact you hypothesize they will create.

The second step in being hypothesis-driven is to apply the scientific method to your hypotheses by creating the fact base to prove or disprove your hypothesis, which then allows you to turn your hypothesis into fact and knowledge. Running with our example, you could prove or disprove your hypothesis on the ideas you think will drive the most impact by executing:

1. An analysis of previous research and the performance of the different ideas 2. A survey where customers rank order the ideas 3. An actual test of the ten ideas to create a fact base on click-through rates and cost

While there are many other ways to validate the hypothesis on your prioritization , I find most people do not take this critical step in validating a hypothesis. Instead, they apply bad logic to many important decisions . An idea pops into their head, and then somehow it just becomes a fact.

One of my favorite lousy logic moments was a CEO who stated,

“I’ve never heard our customers talk about price, so the price doesn’t matter with our products , and I’ve decided we’re going to raise prices.”

Luckily, his management team was able to do a survey to dig deeper into the hypothesis that customers weren’t price-sensitive. Well, of course, they were and through the survey, they built a fantastic fact base that proved and disproved many other important hypotheses.

Why is being hypothesis-driven so important?

Imagine if medicine never actually used the scientific method. We would probably still be living in a world of lobotomies and bleeding people. Many organizations are still stuck in the dark ages, having built a house of cards on opinions disguised as facts, because they don’t prove or disprove their hypotheses. Decisions made on top of decisions, made on top of opinions, steer organizations clear of reality and the facts necessary to objectively evolve their strategic understanding and knowledge. I’ve seen too many leadership teams led solely by gut and opinion. The problem with intuition and gut is if you don’t ever prove or disprove if your gut is right or wrong, you’re never going to improve your intuition. There is a reason why being hypothesis-driven is the cornerstone of problem solving at McKinsey and every other top strategy consulting firm.

How do you become hypothesis-driven?

Most people are idea-driven, and constantly have hypotheses on how the world works and what they or their organization should do to improve. Though, there is often a fatal flaw in that many people turn their hypotheses into false facts, without actually finding or creating the facts to prove or disprove their hypotheses. These people aren’t hypothesis-driven; they are gut-driven.

The conversation typically goes something like “doing this discount promotion will increase our profits” or “our customers need to have this feature” or “morale is in the toilet because we don’t pay well, so we need to increase pay.” These should all be hypotheses that need the appropriate fact base, but instead, they become false facts, often leading to unintended results and consequences. In each of these cases, to become hypothesis-driven necessitates a different framing.

• Instead of “doing this discount promotion will increase our profits,” a hypothesis-driven approach is to ask “what are the best marketing ideas to increase our profits?” and then conduct a marketing experiment to see which ideas increase profits the most.

• Instead of “our customers need to have this feature,” ask the question, “what features would our customers value most?” And, then conduct a simple survey having customers rank order the features based on value to them.

• Instead of “morale is in the toilet because we don’t pay well, so we need to increase pay,” conduct a survey asking, “what is the level of morale?” what are potential issues affecting morale?” and what are the best ideas to improve morale?”

Beyond, watching out for just following your gut, here are some of the other best practices in being hypothesis-driven:

Listen to Your Intuition

Your mind has taken the collision of your experiences and everything you’ve learned over the years to create your intuition, which are those ideas that pop into your head and those hunches that come from your gut. Your intuition is your wellspring of hypotheses. So listen to your intuition, build hypotheses from it, and then prove or disprove those hypotheses, which will, in turn, improve your intuition. Intuition without feedback will over time typically evolve into poor intuition, which leads to poor judgment, thinking, and decisions.

Constantly Be Curious

I’m always curious about cause and effect. At Sports Authority, I had a hypothesis that customers that received service and assistance as they shopped, were worth more than customers who didn’t receive assistance from an associate. We figured out how to prove or disprove this hypothesis by tying surveys to transactional data of customers, and we found the hypothesis was true, which led us to a broad initiative around improving service. The key is you have to be always curious about what you think does or will drive value, create hypotheses and then prove or disprove those hypotheses.

Validate Hypotheses

You need to validate and prove or disprove hypotheses. Don’t just chalk up an idea as fact. In most cases, you’re going to have to create a fact base utilizing logic, observation, testing (see the section on Experimentation ), surveys, and analysis.

Be a Learning Organization

The foundation of learning organizations is the testing of and learning from hypotheses. I remember my first strategy internship at Mercer Management Consulting when I spent a good part of the summer combing through the results, findings, and insights of thousands of experiments that a banking client had conducted. It was fascinating to see the vastness and depth of their collective knowledge base. And, in today’s world of knowledge portals, it is so easy to disseminate, learn from, and build upon the knowledge created by companies.

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A Beginner’s Guide to Hypothesis Testing in Business Analytics

• December 5, 2023
• Analytics , Statistics

Hypothesis testing is a statistical method used to make decisions about a population based on a sample. It helps business analysts draw conclusions about business metrics and make data-driven decisions. This beginner’s guide will provide an introduction to hypothesis testing and how it is applied in business analytics.

What is a Hypothesis?

A hypothesis is an assumption about a population parameter. It is a tentative statement that proposes a possible relationship between two or more variables.

In statistical terms, a hypothesis is an assertion or conjecture about one or more populations. For example, a business hypothesis could be –

“Our social media advertising results in an increase in sales.”

“Customer ratings of our product have decreased this month compared to last month.”

A hypothesis can be:

• Null hypothesis (H0) – a statement that there is no difference or no effect.
• Alternative hypothesis (H1) – a claim about the population that is contradictory to H0.

Hypothesis testing evaluates two mutually exclusive statements (H0 and H1) to determine which statement is best supported by the sample data.

Why Hypothesis Testing is Important in Business

Hypothesis testing allows business analysts to make statistical inferences about a business problem. It is an objective data-driven approach to:

• Evaluate business metrics against a target value. For example – is the current customer satisfaction score significantly lower than our target of 85%?
• Compare business metrics across time periods or categories. For example – has website conversion rate increased this month compared to last month?
• Quantify the impact of business initiatives. For example – did the email marketing campaign result in a significant increase in sales?

Some key benefits of hypothesis testing in business analytics:

• Supports data-driven decision making with statistical evidence.
• Helps save costs by making decisions backed by data insights.
• Enables measurement of success for business initiatives like marketing campaigns, new product launches etc.
• Provides a structured framework for business metric analysis.
• Reduces the influence of individual biases in decision making.

By incorporating hypothesis testing in data analysis, businesses can make sound decisions that are supported by statistical evidence.

Steps in Hypothesis Testing

Hypothesis testing involves the following five steps:

1. State the Hypotheses

This involves stating the null and alternate hypotheses. The hypotheses are stated in a way that they are mutually exclusive – if one is true, the other must be false.

Null hypothesis (H0) – represents the status quo, states that there is no effect or no difference.

Alternative hypothesis (H1) – states that there is an effect or a difference.

For example –

H0: The average customer rating this month is the same as last month.

H1: The average customer rating this month is lower than last month.

2. Choose the Significance Level

The significance level (α) is the probability of rejecting H0 when it is actually true. It is the maximum risk we are willing to take in making an incorrect decision.

Typical values are 0.10, 0.05 or 0.01. A lower α indicates lower risk tolerance. For example α = 0.05 indicates only a 5% risk of concluding there is a difference when actually there is none.

3. Select the Sample and Collect Data

The sample should be representative of the population. Data is collected relevant to the hypotheses – for example, customer ratings this month and last month.

4. Analyze the Sample Data

An appropriate statistical test is applied to analyze the sample data. Common tests used are t-tests, z-tests, ANOVA, chi-square etc. The test provides a test statistic that can be compared against critical values to determine statistical significance.

5. Make a Decision

If the test statistic falls in the rejection region, we reject H0 in favor of H1. Otherwise, we fail to reject H0 and conclude there is not enough evidence against it.

The key question is – “Is the sample data unlikely, assuming H0 is true?” If yes, we reject H0.

Types of Hypothesis Tests

There are two main types of hypothesis tests:

1. Parametric Tests

These tests make assumptions about the shape or parameters of the population distribution.

Some examples are:

• Z-test – Tests a population mean when population standard deviation is known.
• T-test – Tests a population mean when standard deviation is unknown.
• F-test – Compares variances from two normal populations.
• ANOVA – Compares means of two or more populations.

Parametric tests are more powerful as they make use of the distribution characteristics. But the assumptions need to hold true for valid results.

2. Non-parametric Tests

These tests make no assumptions about the exact distribution of the population. They are based on either ranks or frequencies.

• Chi-square test – Tests if two categorical variables are related.
• Mann-Whitney U test – Compares medians from two independent groups.
• Wilcoxon signed-rank test – Compares paired observations or repeated measurements.
• Kruskal Wallis test – Compares medians from two or more groups.

Non-parametric tests are distribution-free but less powerful than parametric tests. They can be used when assumptions of parametric tests are violated.

The choice of statistical test depends on the hypotheses, data type and other factors.

One-tailed and Two-tailed Hypothesis Tests

Hypothesis tests can be one-tailed or two-tailed:

• One-tailed test – When H1 specifies a direction. For example: H0: μ = 10 H1: μ > 10 (or μ < 10)
• Two-tailed test – When H1 simply states ≠, not a specific direction. For example: H0: μ = 10 H1: μ ≠ 10

One-tailed tests have greater power to detect an effect in the specified direction. But we need prior knowledge on the direction of effect for using them.

Two-tailed tests do not assume any direction and are more conservative. They are used when we have no clear prior expectation on the directionality.

Interpreting Hypothesis Test Results

Hypothesis testing results can be interpreted based on:

• p-value – Probability of obtaining sample results if H0 is true. Small p-value (< α) indicates significant evidence against H0.
• Confidence intervals – Range of likely values for the population parameter. If it does not contain the H0 value, we reject H0.
• Test statistic – Standardized value computed from sample data. Compared against critical values to determine statistical significance.
• Effect size – Quantifies the magnitude or size of effect. Important for interpreting practical significance.

Hypothesis testing indicates whether an effect exists or not. Measures like effect size and confidence intervals provide additional insights on the observed effect.

Common Errors in Hypothesis Testing

Some common errors to watch out for:

• Having unclear, ambiguous hypotheses.
• Choosing an inappropriate significance level α.
• Using the wrong statistical test for data analysis.
• Interpreting a non-significant result as proof of no effect. Absence of evidence is not evidence of absence.
• Concluding practical significance from statistical significance. Small p-values don’t always imply practical business impact.
• Multiple testing without adjustment leading to elevated Type I errors.
• Stopping data collection prematurely when a significant result is obtained.
• Overlooking effect sizes, confidence intervals while focusing solely on p-values.

Proper application of hypothesis testing methodology minimizes such errors and improves decision making.

Real-world Example of Hypothesis Testing

Let’s take an example of using hypothesis testing in business analytics:

A retailer wants to test if launching a new ecommerce website has resulted in increased online sales.

The retailer gathers weekly sales data before and after the website launch:

H0: Launching the new website did not increase the average weekly online sales

H1: Launching the new website increased the average weekly online sales

Significance level is chosen as 0.05. Appropriate parametric / non-parametric test is selected based on data. Test results show that the p-value is 0.01, which is less than 0.05.

Therefore, we reject the null hypothesis and conclude that the new website launch has resulted in significantly increased online sales at the 5% significance level.

The analyst also computes a 95% confidence interval for the difference in sales before and after website launch. The retailer uses these insights to make data-backed decisions on marketing budget allocation between traditional and digital channels.

Hypothesis testing provides a formal process for making statistical decisions using sample data. It helps assess business metrics against benchmarks, quantify impact of initiatives and compare performance across time periods or segments. By embedding hypothesis testing in analytics, businesses can derive actionable insights for data-driven decision making.

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Your Strategy Should Be a Hypothesis You Constantly Adjust

• Amy C. Edmondson
• Paul J. Verdin

Too many leaders think it should be set in stone.

Strategies must be constantly adjusted to incorporate information from operations and the market. Research on recent dramatic cases of strategic failure in different industries and involving vastly different business models and strategies shows a common pattern: What started as small gaps in execution spiraled into business failures when initial strategies were not altered based on new information provided by experience. These companies’ strategies were viewed by their top executives as analytically sound; performance gaps were blamed on execution. An alternative perspective on strategy and execution — one that is more in tune with the nature of value creation in a world marked by volatility, uncertainty, complexity, and ambiguity — conceives of strategy as a hypothesis rather than a plan. Like all hypotheses, it starts with situation assessment and analysis — strategy’s classic tools. Also like all hypotheses, it must be tested through action. With this lens, encounters with customers provide data that is of ongoing interest to senior executives — vital inputs to dynamic strategy formulation. The authors call this approach strategy as learning , which contrasts sharply with the view of strategy as a stable, analytically rigorous plan for execution in the market. Strategy as learning is an executive activity characterized by ongoing cycles of testing and adjusting, fueled by data that can only be obtained through execution.

The widely accepted view that strategy and execution are separable activities sets companies up for failure in a fast-paced world.

• Amy C. Edmondson is the Novartis Professor of Leadership and Management at Harvard Business School. Her latest book is Right Kind of Wrong: The Science of Failing Well (Atria Books, forthcoming in September 2023).
• Paul J. Verdin is the chair in strategy and organization at Solvay Brussels School of Economics and Management.

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

Introduction

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

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

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

What is the simple definition of a hypothesis?

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

What is the hypothesis for in research?

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

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

What is an example of a hypothesis?

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

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

What makes a good hypothesis?

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

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

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

Null hypothesis

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

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

Alternative hypothesis

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

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

Simple hypothesis

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

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

Complex hypothesis

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

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

Directional hypothesis

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

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

Statistical hypothesis

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

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

Empirical hypothesis

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

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

Causal hypothesis

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

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

Associative hypothesis

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

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

Relational hypothesis

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

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

Logical hypothesis

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

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

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

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

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

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

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

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

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

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

Literature review

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

Research methods

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

Preliminary research

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

Data analysis

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

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7.5: Full Hypothesis Test Examples

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Tests on Means

Example \(\PageIndex{1}\)

Jeffrey, as an eight-year old, established a mean time of 16.43 seconds for swimming the 25-yard freestyle.

His dad, Frank, thought that Jeffrey could swim the 25-yard freestyle faster using goggles. Frank bought Jeffrey a new pair of expensive goggles and timed Jeffrey for 15 25-yard freestyle swims . For the 15 swims, Jeffrey's mean time was 16 seconds , with a standard deviation of 0.8 seconds . Frank thought that the goggles helped Jeffrey to swim faster than the 16.43 seconds. Conduct a hypothesis test using test statistics and \(p\)-values with a preset \(\alpha = 0.05\).

Set up the Hypothesis Test:

Since the problem is about a mean, this is a test of a single population mean .

Set the null and alternative hypothesis:

In this case there is an implied challenge or claim. This is that the goggles will reduce the swimming time. The effect of this is to set the hypothesis as a one-tailed test. The claim will always be in the alternative hypothesis because the burden of proof always lies with the alternative. Remember that the status quo must be defeated with a high degree of confidence, in this case 95% confidence. The null and alternative hypotheses are thus:

\(H_0: \mu \geq 16.43\)  \(H_a: \mu < 16.43\)

For Jeffrey to swim faster, his time should be less than 16.43 seconds. The "<" tells you this is left-tailed.

Determine the distribution needed:

Random variable: \(\overline x\) = the mean time to swim the 25-yard freestyle.

Distribution for the test statistic:

The sample size is less than 30 and we do not know the population standard deviation so this is a t -test. The proper formula is: \(t_{obs}=\frac{\overline{x}-\mu_{0}}{s / \sqrt{n}}\)

\(\mu_ 0 = 16.43\) comes from \(H_0\) and not the data. \(\overline x = 16\), \(s = 0.8\), and \(n = 15\).

Our step 2, setting the level of confidence, has already been determined by the problem, \(\alpha\) of .05 corresponds to a 95% confidence level. It is worth thinking about the meaning of this choice. The Type I error is to conclude that Jeffrey swims the 25-yard freestyle, on average, in less than 16.43 seconds when, in fact, he actually swims the 25-yard freestyle, on average, in 16.43 seconds or more. (Reject the null hypothesis when the null hypothesis is true.) For this case the only concern with a Type I error would seem to be that Jeffrey’s dad may fail to bet on his son’s victory because he does not have appropriate confidence in the effect of the goggles.

To find the critical value we need to select the appropriate test statistic. We have concluded that this is a t -test on the basis of the sample size and that we are interested in a population mean. We can now draw the graph of the t -distribution and mark the critical value. For this problem the degrees of freedom are n-1, or 14. Looking up 14 degrees of freedom at the 0.05 column of the t -table we find 1.761. This is the critical value and we can put this on our graph.

Step 3 is the calculation of the test statistic using the formula we have selected. We find that the observed test statistic is -2.08, meaning that the sample mean is 2.08 standard errors below the hypothesized mean of 16.43.

\[t_{obs}=\frac{\overline{x}-\mu_{0}}{s / \sqrt{n}}=\frac{16-16.43}{.8 / \sqrt{15}}=-2.08\nonumber\]

Figure \(\PageIndex{1}\)

Step 4 has us compare the test statistic and the critical value and mark these on the graph. We see that the test statistic is in the tail and thus we move to step 4 and reach a conclusion. The probability that an average time of 16 minutes could come from a distribution with a population mean of 16.43 minutes is too unlikely to have occurred under the null hypothesis. We reject the null.

Step 5 has us state our conclusions first formally and then less formally. A formal conclusion would be stated as: “With a 95% level of confidence we reject the null hypothesis that the swimming time with goggles comes from a distribution with a population mean time of 16.43 minutes.” Less formally, “With 95% confidence, we believe that the goggles improved swimming speed".

If we wished to use the \(p\)-value system of reaching a conclusion we would calculate the statistic and take the additional step to find the probability of being 2.08 standard errors from the mean on a t -distribution. The \(p\)-value interval is (.025, .05), that we get by looking up the one-tailed probabilities associated with the closest t -scores (1.761 and 2.145) to the observed test statistic (-2.08) in the relevant df row of 14 in the t -table. Comparing this interval to the significance level of .05 we see that we reject the null. The \(p\)-value has been put on the graph as the shaded area beyond -2.08 and it shows that it is smaller than the hatched area which is the \(\alpha\) level of 0.05. Both methods reach the same conclusion that we reject the null hypothesis.

Exercise \(\PageIndex{1}\)

The mean throwing distance of a football for Marco, a high school freshman quarterback, is 40 yards, with a standard deviation of two yards. The team coach tells Marco to adjust his grip to get more distance. The coach records the distances for 20 throws. For the 20 throws, Marco’s mean distance was 45 yards. The coach thought the different grip helped Marco throw farther than 40 yards. Conduct a hypothesis test using a preset \(\alpha = 0.05\). Assume the throw distances for footballs are normal.

First, determine what type of test this is, set up the hypothesis test, find the \(p\)-value, sketch the graph, and state your conclusion.

Example \(\PageIndex{2}\)

Jane has just begun her new job as on the sales force of a very competitive company. In a sample of 16 sales calls it was found that she closed the contract for an average value of 108 dollars with a standard deviation of 12 dollars. Company policy requires that new members of the sales force must exceed an average of $100 per contract during the trial employment period. Can we conclude that Jane has met this requirement at the significance level of 5%?

• \(H_0: \mu \leq 100\) \(H_a: \mu > 100\) The null and alternative hypothesis are for the parameter \(\mu\) because the number of dollars of the contracts is a continuous random variable. Also, this is a one-tailed test because the company has only an interested if the number of dollars per contact is below a particular number not "too high" a number. This can be thought of as making a claim that the requirement is being met and thus the claim is in the alternative hypothesis.
• Test statistic: \(t_{obs}=\frac{\overline{x}-\mu_{0}}{\frac{s}{\sqrt{n}}}=\frac{108-100}{\left(\frac{12}{\sqrt{16}}\right)}=2.67\)
• Critical value: \(t_\alpha=1.753\) with \(n-1\) degrees of freedom = 15

The test statistic is a Student's t because the sample size is below 100; therefore, we cannot use the normal distribution. Comparing the observed value of the test statistic and the critical value of t at a 5% significance level, we see that the observed value is in the tail of the distribution. Thus, we conclude that 108 dollars per contract is significantly larger than the hypothesized value of 100 and thus we must reject the null hypothesis. There is evidence that Jane's performance meets company standards.

Figure \(\PageIndex{2}\)

Exercise \(\PageIndex{2}\)

It is believed that a stock price for a particular company will grow at a rate of $5 per week with a standard deviation of $1. An investor believes the stock won’t grow as quickly. The changes in stock price is recorded for ten weeks and are as follows: $4, $3, $2, $3, $1, $7, $2, $1, $1, $2. Perform a hypothesis test using a 5% level of significance. State the null and alternative hypotheses, state your conclusion, and identify the Type I and Type II errors.

Example \(\PageIndex{3}\)

A manufacturer of salad dressings uses machines to dispense liquid ingredients into bottles that move along a filling line. The machine that dispenses salad dressings is working properly when 8 ounces are dispensed. Suppose that the average amount dispensed in a particular sample of 35 bottles is 7.91 ounces with a variance of 0.03 ounces squared, \(s^2\). Is there evidence that the machine should be stopped and production wait for repairs? The lost production from a shutdown is potentially so great that management feels that the level of confidence in the analysis should be 99%.

Again we will follow the steps in our analysis of this problem.

STEP 1 : Set the null and alternative hypothesis.

The random variable is the quantity of fluid placed in the bottles. This is a continuous random variable and the parameter we are interested in is the mean. Our hypothesis therefore is about the mean. In this case we are concerned that the machine is not filling properly. From what we are told it does not matter if the machine is over-filling or under-filling, both seem to be an equally bad error. This tells us that this is a two-tailed test: if the machine is malfunctioning it will be shutdown regardless if it is from over-filling or under-filling. The null and alternative hypotheses are thus:

\[H_0:\mu=8\nonumber\]

\[Ha:\mu \neq 8\nonumber\]

STEP 2 : Decide the level of significance and draw the graph showing the critical value.

This problem has already set the level of confidence at 99%. The decision seems an appropriate one and shows the thought process when setting the significance level. Management wants to be very certain, as certain as probability will allow, that they are not shutting down a machine that is not in need of repair. To draw the distribution and the critical value, we need to know which distribution to use. Because the sample size is under 100, the appropriate distribution is the t -distribution and the relevant critical value is 2.750 from the t -table at 0.005 column and 30 degrees of freedom (closest available row to our actual 34 df here). We need to draw the graph and mark these points.

STEP 3 : Calculate sample parameters and the test statistic.

The sample parameters are provided, the sample mean is 7.91 and the sample variance is .03 and the sample size is 35. We need to note that the sample variance was provided, not the sample standard deviation, which is what we need for the formula. Remembering that the standard deviation is simply the square root of the variance, we therefore know the sample standard deviation, \(s\), is 0.173. With this information we can calculate the test statistic as -3.07, and mark it on the graph.

\[t_{obs}=\frac{\overline{x}-\mu_{0}}{s / \sqrt{n}}=\frac{7.91-8}{\cdot 173 / \sqrt{35}}=-3.07\nonumber\]

STEP 4 : Compare test statistic and the critical values.

Now we compare the test statistic and the critical value by placing the test statistic on the graph. The test statistic is in the tail, decidedly greater than the critical value of 2.750. We note that even the very small difference between the hypothesized value and the sample value is still a large number of standard errors. The sample mean is only 0.08 ounces different from the required level of 8 ounces, but it is 3+ standard errors away from the required 8 ounces, and thus we reject the null hypothesis.

STEP 5 : Reach a conclusion.

Three standard errors of a test statistic will guarantee that the test will fail. The probability that anything is beyond three standard errors of a hypothesized null value - given a large enough sample size - is close to zero. Looking at the closest t -scores in df =30 row in the t -table, we get the \(p\)-value interval of (.01, .002) after doubling the one-tailed probabilities of .005 and .001. Our formal conclusion would be “At a 99% level of confidence, we reject the null hypothesis that the sample mean came from a distribution with a mean of 8 ounces”. Or less formally, and getting to the point, “At a 99% level of confidence, we conclude that the machine is under-filling the bottles and is in need of repair”.

Hypothesis Test for Proportions

Just as there were confidence intervals for proportions, or more formally, the population parameter \(P\), there is the ability to test hypotheses concerning \(P\).

The estimated value (point estimate) for \(P\) is \(P^{\prime}\) where \(P^{\prime} = x/n\), \(x\) is the number of observations in the category of interest in the sample and \(n\) is the sample size.

When you perform a hypothesis test of a population proportion \(P\), you take a random sample from the population. To ensure normality of the distribution, sampling must be random and the total sample size must be greater than 100. There is no distribution that can correct for this small sample bias and thus if these conditions are not met we simply cannot test the hypothesis with the data available at that time. We met this condition when we were first estimating confidence intervals for \(P\).

Again, we begin with the modified standardizing formula:

\[z=\frac{P^{\prime}-P}{\sqrt{\frac{P(1-P)}{n}}}\nonumber\]

Substituting \(P_0\), the hypothesized value of \(P\), we have:

\[z_{obs}=\frac{P^{\prime}-P_{0}}{\sqrt{\frac{P_{0} (1-P_{0})}{n}}}\nonumber\]

This is the test statistic for testing hypothesized values of \(P\), where the null and alternative hypotheses take one of the following forms:

Table \(\PageIndex{1}\)

The decision rule stated above applies here also: if the calculated value of \(z_{obs}\) shows that the sample proportion is "too many" standard errors from the hypothesized proportion, the null hypothesis is rejected. The decision as to what is "too many" is pre-determined by the analyst depending on the level of significance required in the test.

Example \(\PageIndex{4}\)

The mortgage department of a large bank is interested in the nature of loans of first-time borrowers. This information will be used to tailor their marketing strategy. They believe that 50% of first-time borrowers take out smaller loans than other borrowers. They perform a hypothesis test to determine if the percentage is different from 50% . They sample 101 first-time borrowers and find 54 of these loans are smaller that the other borrowers. For the hypothesis test, they choose a 5% level of significance.

\(H_0: P = 0.50\)  \(H_a: P \neq 0.50\)

The words "is different from" tell you this is a two-tailed test. The Type I and Type II errors are as follows: The Type I error is to conclude that the proportion of borrowers is different from 50% when, in fact, the proportion is actually 50%. (Reject the null hypothesis when the null hypothesis is true). The Type II error is there is not enough evidence to conclude that the proportion of first time borrowers differs from 50% when, in fact, the proportion does differ from 50%. (You fail to reject the null hypothesis when the null hypothesis is false.)

STEP 2 : Decide the level of significance and draw the graph showing the critical value

The level of confidence has been set by the problem at 95%. Because this is two-tailed test one-half of the \(\alpha\) value will be in the upper tail and one-half in the lower tail as shown on the graph. The critical value for the normal distribution at the 95% level of confidence is 1.96. This can easily be found on the Student’s t -table at the very bottom at infinite degrees of freedom remembering that at infinity the t -distribution is the normal distribution. Of course, the value can also be found on the standard normal table but you have go looking for the tail probability, \(\alpha\)/2, inside the body of the table and then read out to the sides and top for the number of standard errors.

Figure \(\PageIndex{3}\)

STEP 3 : Calculate the sample parameters and critical value of the test statistic.

The test statistic is a normal distribution, \(z\), for testing proportions and is:

\[z=\frac{P^{\prime}-P_{0}}{\sqrt{\frac{P_{0} (1-P_{0})}{n}}}=\frac{.53-.50}{\sqrt{\frac{.5(.5)}{101}}}=0.60\nonumber\]

For this case, the sample of 101 found 54 first-time borrowers were different from other borrowers. The sample proportion, \(P^{\prime} = 54/101= 0.53\) The test question, therefore, is : “Is 0.53 significantly different from 0.50?” Putting these values into the formula for the test statistic we find that 0.53 is only 0.60 standard errors away from 0.50. This is barely off of the mean of the standard normal distribution of zero. There is virtually no difference from the sample proportion and the hypothesized proportion in terms of standard errors.

STEP 4 : Compare the test statistic and the critical value.

The observed value is well within the critical values of \(\pm 1.96\) standard errors and thus we cannot reject the null hypothesis. To reject the null hypothesis we need significant evidence of difference between the hypothesized value and the sample value. In this case the sample value is very nearly the same as the hypothesized value measured in terms of standard errors.

The formal conclusion would be “At a 95% level of confidence we cannot reject the null hypothesis that 50% of first-time borrowers have the same size loans as other borrowers”. Less formally, we would say that “There is no evidence that one-half of first-time borrowers are significantly different in loan size from other borrowers”. Notice the length to which the conclusion goes to include all of the conditions that are attached to the conclusion. Statisticians, for all the criticism they receive, are careful to be very specific even when this seems trivial. Statisticians cannot say more than they know and the data constrain the conclusion to be within the metes and bounds of the data.

Exercise \(\PageIndex{3}\)

A teacher believes that 85% of students in the class will want to go on a field trip to the local zoo. She performs a hypothesis test to determine if the percentage is the same or different from 85%. The teacher samples 104 students and 89 reply that they would want to go to the zoo. For the hypothesis test, use a 1% level of significance.

Example \(\PageIndex{5}\)

Suppose a consumer group suspects that the proportion of households that have three or more cell phones is 30%. A cell phone company has reason to believe that the proportion is not 30%. Before they start a big advertising campaign, they conduct a hypothesis test using 90% confidence. Their marketing people survey 150 households with the result that 43 of the households have three or more cell phones.

Here is an abbreviated version of the system to solve hypothesis tests applied to a test on a proportions.

\[H_0 : P = 0.3 \nonumber\]

\[H_a : P \neq 0.3 \nonumber\]

\[n = 150\nonumber\]

\[P^{\prime}=\frac{x}{n}=\frac{43}{150}=0.287\nonumber\]

\[z_{obs}=\frac{P^{\prime}-P_{0}}{\sqrt{\frac{P_{0} (1-P_{0})}{n}}}=\frac{0.287-0.3}{\sqrt{\frac{.3(.7)}{150}}}=0.347\nonumber\]

At a confidence level of 90% we cannot reject the null hypothesis that the consumer group is correct.

Figure \(\PageIndex{4}\)

Example \(\PageIndex{6}\)

In a study of 420,019 cell phone users, 172 of the subjects developed brain cancer. Test the claim that cell phone users developed brain cancer at a greater rate than that for non-cell phone users (the rate of brain cancer for non-cell phone users is 0.0340%). Since this is a critical issue, use a 0.005 significance level. Explain why the significance level should be so low in terms of a Type I error.

We need to conduct a hypothesis test on the claimed cancer rate. Our hypotheses will be:

If we commit a Type I error, we are essentially accepting an incorrect claim. Since the claim describes cancer-causing environments, we want to minimize the chances of incorrectly identifying causes of cancer.

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Hypothesis testing in business administration.

• Rand R. Wilcox Rand R. Wilcox Department of Psychology, University of Southern California
• https://doi.org/10.1093/acrefore/9780190224851.013.279
• Published online: 27 August 2020

Hypothesis testing is an approach to statistical inference that is routinely taught and used. It is based on a simple idea: develop some relevant speculation about the population of individuals or things under study and determine whether data provide reasonably strong empirical evidence that the hypothesis is wrong. Consider, for example, two approaches to advertising a product. A study might be conducted to determine whether it is reasonable to assume that both approaches are equally effective. A Type I error is rejecting this speculation when in fact it is true. A Type II error is failing to reject when the speculation is false. A common practice is to test hypotheses with the type I error probability set to 0.05 and to declare that there is a statistically significant result if the hypothesis is rejected.

There are various concerns about, limitations to, and criticisms of this approach. One criticism is the use of the term significant . Consider the goal of comparing the means of two populations of individuals. Saying that a result is significant suggests that the difference between the means is large and important. But in the context of hypothesis testing it merely means that there is empirical evidence that the means are not equal. Situations can and do arise where a result is declared significant, but the difference between the means is trivial and unimportant. Indeed, the goal of testing the hypothesis that two means are equal has been criticized based on the argument that surely the means differ at some decimal place. A simple way of dealing with this issue is to reformulate the goal. Rather than testing for equality, determine whether it is reasonable to make a decision about which group has the larger mean. The components of hypothesis-testing techniques can be used to address this issue with the understanding that the goal of testing some hypothesis has been replaced by the goal of determining whether a decision can be made about which group has the larger mean.

Another aspect of hypothesis testing that has seen considerable criticism is the notion of a p -value. Suppose some hypothesis is rejected with the Type I error probability set to 0.05. This leaves open the issue of whether the hypothesis would be rejected with Type I error probability set to 0.025 or 0.01. A p -value is the smallest Type I error probability for which the hypothesis is rejected. When comparing means, a p -value reflects the strength of the empirical evidence that a decision can be made about which has the larger mean. A concern about p -values is that they are often misinterpreted. For example, a small p -value does not necessarily mean that a large or important difference exists. Another common mistake is to conclude that if the p -value is close to zero, there is a high probability of rejecting the hypothesis again if the study is replicated. The probability of rejecting again is a function of the extent that the hypothesis is not true, among other things. Because a p -value does not directly reflect the extent the hypothesis is false, it does not provide a good indication of whether a second study will provide evidence to reject it.

Confidence intervals are closely related to hypothesis-testing methods. Basically, they are intervals that contain unknown quantities with some specified probability. For example, a goal might be to compute an interval that contains the difference between two population means with probability 0.95. Confidence intervals can be used to determine whether some hypothesis should be rejected. Clearly, confidence intervals provide useful information not provided by testing hypotheses and computing a p -value. But an argument for a p -value is that it provides a perspective on the strength of the empirical evidence that a decision can be made about the relative magnitude of the parameters of interest. For example, to what extent is it reasonable to decide whether the first of two groups has the larger mean? Even if a compelling argument can be made that p -values should be completely abandoned in favor of confidence intervals, there are situations where p -values provide a convenient way of developing reasonably accurate confidence intervals. Another argument against p -values is that because they are misinterpreted by some, they should not be used. But if this argument is accepted, it follows that confidence intervals should be abandoned because they are often misinterpreted as well.

Classic hypothesis-testing methods for comparing means and studying associations assume sampling is from a normal distribution. A fundamental issue is whether nonnormality can be a source of practical concern. Based on hundreds of papers published during the last 50 years, the answer is an unequivocal Yes. Granted, there are situations where nonnormality is not a practical concern, but nonnormality can have a substantial negative impact on both Type I and Type II errors. Fortunately, there is a vast literature describing how to deal with known concerns. Results based solely on some hypothesis-testing approach have clear implications about methods aimed at computing confidence intervals. Nonnormal distributions that tend to generate outliers are one source for concern. There are effective methods for dealing with outliers, but technically sound techniques are not obvious based on standard training. Skewed distributions are another concern. The combination of what are called bootstrap methods and robust estimators provides techniques that are particularly effective for dealing with nonnormality and outliers.

Classic methods for comparing means and studying associations also assume homoscedasticity. When comparing means, this means that groups are assumed to have the same amount of variance even when the means of the groups differ. Violating this assumption can have serious negative consequences in terms of both Type I and Type II errors, particularly when the normality assumption is violated as well. There is vast literature describing how to deal with this issue in a technically sound manner.

• hypothesis testing
• significance
• confidence intervals
• nonnormality
• bootstrap methods
• robust estimators

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

Prevent plagiarism, run a free check.

Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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

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

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

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What if we found ourselves building something that nobody wanted? In that case, what did it matter if we did it on time and on budget? —Eric Ries, The Lean Startup [1]

An Epic is a significant solution development initiative.

Portfolio epics are typically cross-cutting, typically spanning multiple Value Streams and PIs . To accelerate learning and development and reduce risk, SAFe recommends applying the Lean Startup build-measure-learn cycle for these epics.

This article describes the portfolio epic’s definition, approval, and implementation. Agile Release Train (ART) and Solution Train epics, which follow a similar pattern, are described briefly at the end of this article.

There are two types of epics, each of which may occur at different levels of the Framework. Business epics directly deliver business value, while enabler epics advance the  Architectural Runway  to support upcoming business or technical needs.

It’s important to note that epics are not merely a synonym for projects; they operate quite differently, as Figure 1 highlights.

SAFe discourages using the project funding model (refer to the Lean Portfolio Management article). Instead, the funding to implement epics is allocated directly to the value streams within a portfolio. Moreover, Agile Release Trains (ARTs) develop and deliver epics following the Lean Startup Cycle discussed later in this article (Figure 6).

Defining Epics

Since epics are some of the most significant enterprise investments, stakeholders must agree on their intent and definition. Figure 2 provides an epic hypothesis statement template for capturing, organizing, and communicating critical information about an epic.

Epics above the approval Guardrail are made visible, developed, and managed through the  Portfolio Kanban system, where they proceed through various states of maturity until they’re approved or rejected. Before being committed to implementation, epics require analysis. Epic Owners take responsibility for the critical collaborations needed for Business Epics, while  Enterprise Architects  typically guide the Enabler epics that support the technical considerations for business epics.

Creating the Lean Business Case

The result of the epic analysis is a Lean business case (Figure 3).

The LPM reviews the Lean business case to make a go/no-go decision for the epic. Once approved, portfolio epics move to the Ready state of the Portfolio Kanban. When capacity and budget become available from one or more ARTs, the Epic is pulled into implementation. The Epic Owner is responsible for working with Product  and  Solution Management  and  System Architects  to split the epic into Features or Capabilities during backlog refinement. Epic Owners help prioritize these items in their respective backlogs and have ongoing responsibilities for their development and follow-up.

Defining the MVP

Analysis of an epic includes the definition of a Minimum Viable Product (MVP) for the epic. In the context of SAFe, an MVP is an early and minimal version of a new product or business  Solution  used to prove or disprove the epic hypothesis. Unlike storyboards, prototypes, mockups, wireframes, and other exploratory techniques, the MVP is an actual product that real customers can use to generate validated learning.

Estimating Epic Costs

As Epics progress through the Portfolio Kanban, the LPM team will eventually need to understand the potential investment required to realize the hypothesized value. This analysis requires a meaningful estimate of the cost of the MVP, and the forecasted cost of the full implementation should the epic hypothesis be proven true.

• The  MVP cost  ensures the portfolio is budgeting enough money to prove or disprove the Epic hypothesis. It helps ensure that LPM makes sufficient investments in innovation aligned with Lean budget guardrails.
• The forecasted implementation cost considers ROI analysis, helping determine if the business case is sound, and allows the LPM team to prepare for potential adjustments to value stream budgets.

The Epic owner determines the amount of the MVP’s investment in collaboration with other key stakeholders. This investment should be sufficient to prove or disprove the MVP’s hypothesis. Once approved, the value stream cannot spend more than the defined investment cost to build and evaluate the MVP. If the value stream has evidence that this cost will be exceeded during epic implementation, further work on the epic should be discussed with LPM before exceeding the MVP’s estimated cost.

Estimating Implementation Cost

Considerable strategic efforts often require collaboration with external Suppliers to develop Solutions. The MVP and the anticipated full implementation cost estimates should include internal costs and forecasted external Supplier expenses.

Estimating epics in the early stages can be challenging since there is limited data and learning at this point. As illustrated in Figure 4, ‘T-shirt sizing’ is a simple way to estimate epics, especially in the early stages:

• A cost range is established for each t-shirt size using historical data
• The gaps in the cost ranges reflect the uncertainty of estimates and avoid excessive discussion around edge cases
• Each portfolio must determine the relevant cost range for the t-shirt sizes

The Epic Owner can incrementally refine the total implementation cost as the MVP is built and learning occurs.

Supplier Costs

An epic investment often includes the contribution and cost from suppliers, whether internal or external. Ideally, enterprises engage external suppliers via Agile contracts, which supports estimating the costs of a supplier’s contribution to a specific epic. For more on this topic, see the Agile Contracts  extended guidance article.

Forecasting an Epic’s Duration

While it can be challenging to forecast the duration of an epic implemented by a mix of internal ARTs and external suppliers, an understanding of the forecasted duration of the epic is critical to the proper functioning of the portfolio.

Like an epic’s cost, its duration isn’t easy to forecast as it includes several components, such as internal duration, supplier duration, and the collaborations and interactions between the internal and external teams. Practically, unless the epic is wholly outsourced, LPM can focus on forecasts of the internal ARTs affected by the epic, as they are expected to coordinate work with external suppliers.

Forecasting an epic’s duration requires an understanding of three data points:

• An epic’s estimated size in story points for each affected ART can also be calculated using T-shirt sizes and replacing the cost range with a story point range.
• The historical velocity of the impacted ARTs.
• The percent (%) capacity allocation that ARTs can dedicate to working on the epic. This allocation typically results from negotiation between Product and Solution Management, Epic Owners, and LPM.

In the example shown in Figure 5, a portfolio has a substantial enabler epic that affects three ARTs, and LPM seeks to gain an estimate of the forecasted number of PIs.

ART 1 has estimated the epic’s size as 2,000 – 2,500 points. Product Management determines that ART 1 can allocate 40% of total capacity toward implementing its part of the epic. With a historical velocity of 1,000 story points per PI, ART 1 forecasts between five to seven PIs for the epic.

After repeating these calculations for each ART, the Epic Owner can see that some ARTs will likely be ready to release on demand earlier than others. However, the forecasted duration to deliver the entire epic across all ARTs will likely be between six and eight PIs. If this forecast does not align with business needs, negotiations such as adjusting capacity allocations or increasing the budget for suppliers will ensue. The Epic Owner updates the forecasted completion once work begins on the epic.

Implementing Epics

The SAFe Lean startup strategy recommends a highly iterative build-measure-learn cycle for product innovation and strategic investments. This approach for implementing epics provides the economic and strategic advantages of a Lean startup by managing investment and risk incrementally while leveraging the flow and visibility benefits of SAFe (Figure 6).

Gathering the data necessary to prove or disprove the epic hypothesis is highly iterative. These iterations continue until a data-driven result is obtained or the teams consume the entirety of the MVP budget. In general, the result of a proven hypothesis is an MVP suitable for continued investment by the value streams. Otherwise, any further investment requires the creation of a new epic.

After it’s approved for implementation, the Epic Owner works with the Agile Teams  to begin the development activities needed to realize the business outcomes hypothesis for the epic:

• If the hypothesis is true , the epic enters the persevere state, which will drive more work by implementing additional features and capabilities. ARTs manage any further investment in the epic via ongoing WSJF feature prioritization of the ART Backlog . Local features identified by the ART, and those from the epic, compete during routine WSJF reprioritization.
• If the hypothesis is false , Epic Owners can decide to pivot by creating a new epic for LPM review or dropping the initiative altogether and switching to other work in the backlog.

After evaluating an epic’s hypothesis, it may or may not be considered a portfolio concern. However, the Epic Owner may have ongoing stewardship and follow-up responsibilities.

Lean budgets’ empowerment and decentralized decision-making depend on Guardrails for specific checks and balances. Value stream KPIs and other metrics also support guardrails to keep the LPM informed of the epic’s progress toward meeting its business outcomes hypothesis.

ART and Solution Train Epics

Epics may originate from local ARTs or Solution Trains, often starting as initiatives that warrant LPM attention because of their significant business impact or initiatives that exceed the epic threshold. ART and Solution Train epics may also originate from portfolio epics that must be split to facilitate incremental implementation. Like any other epics, ART and Solution Train epics deserve a Lean business case that captures these significant investments’ purpose and expected benefits. The ART and Solution Train Backlogs article describes methods for managing the flow of local epics that do not meet the criteria for portfolio attention.

Last update: 6 September 2023

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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How to write a research hypothesis

Last updated

19 January 2023

Reviewed by

Miroslav Damyanov

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

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

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

I nteresting

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

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

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

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• Types of research hypotheses

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

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

Null hypothesis

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

Alternative hypothesis

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

Simple hypothesis

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

Complex hypothesis

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

Directional hypothesis

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

Non-directional hypothesis

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

Associative hypothesis

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

Logical hypothesis

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

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

Empirical hypothesis

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

Statistical hypothesis

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

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

Causal hypothesis

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

Descriptive hypothesis

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

• What makes an effective research hypothesis?

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

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

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

• How to develop a good research hypothesis

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

the parameter you'll test

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

the hypothesized parameter value

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

Single mean (μ)

Paired means (μd)

Single proportion (p)

Difference between two independent means (μ1−μ2)

Difference between two proportions (p1−p2)

Simple linear regression slope (β)

Correlation (ρ)

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

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

• What is the correct format for writing a hypothesis?

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

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

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

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

• What are the five steps of a hypothesis?

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

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

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

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

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

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

What is the Red Queen hypothesis?

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

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

• Getting started with your research

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

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Hypotheses & Experiments

The Hypotheses & Experiments template is the heart of Business Design and helps you to plan your Validate Phase. Learn more about key elements, go through further instructions and download the template for your team.

1. Overview

The Hypotheses & Experiments template can help you reveal critical questions (= exploration ) and hypotheses (= validation ) inherent in your new business model, product, service or software, which are both uncertain and important for the success of the innovation endeavour. These questions and hypotheses often address “antilogs” around technical feasibility, economic viability or future customer behaviour. Moreover, this canvas provides space to plan how to explore or test the questions and hypotheses as efficiently as possible with experiments before or after launch. Do not test ideas! Test hypotheses that your ideas are based on!

Translate only the most important and uncertain antilogs (= test focus) into either a question or a testable hypothesis. This, however, is not an easy task, especially at the beginning of innovation projects. We usually start with explorative questions and high-level hypotheses that are hard to test. Over time, we are eventually able to nail them down to very specific but still relevant statements. Here is an example:

We believe that payment by credit cards will be accepted by the majority of our customers.

Every question or hypothesis needs an experiment in order to be explored or tested. An experiment defines a sequence of actions needed to collect data and measure the threshold: <action><measurement><timeframe>

We integrate credit card payments into our check-out process for X customers and count the usage within the next 3 weeks.

Many experiments require a Prototype (pre launch), whereas some experiments can only be conducted after launching Lean Offerings (post launch).

When validating a hypothesis, we define a threshold that determines when we are convinced that a hypothesis is "true" or "false".

We are convinced, if more than 50% of our customers who ordered products in the given timeframe used credit cards as payment method.

2. Layout & Download

3. Key Elements

This tool is the heart of Business Design !  Here is where the magic happens. Invest as much time in coming up with a good research design as you have invested in designing your actual product, software or business model. Think about your sponsor: The results of your experiments is THE source of wisdom for your sponsor to make decision on whether or not to further invest in the project. Don't fool yourself! Provide your sponsor the right support to make up his / her mind.

4. Usage Scenarios

Gathering and prioritizing uncertainties

Defining questions, hypotheses and experiments based on uncertainties

5. Instructions for Coaches

The Hypotheses & Experiments template is a good way to gather all kinds of uncertainties related to the business model.

The key is to identify as many uncertainties as possible and to then sort them by analogs or antilogs. Mark the uncertainties in your business model first.

It cannot be said that antilogs are more important than analogs. Analogs make life easier because one can simply copy from others. However, if only analogs and no antilogs were identified the sprint would stop because no further validation would be needed.

Keep the order to first gather uncertainties, to then sort them by analogs or antilogs and to then sort the antilogs by uncertainty and importance. This is one of the reasons why we use Post-its because we can move them around on the template.

Be really precise how questions or hypotheses are phrased. Beginning every Post-it with "We believe that..." or "We don't know..." is an additional help to reflect whether validation or exploration are the right method.

6. Q & A

What is the difference between "Exploration" and "Validation"? Exploration is a scientific approach of empirical research to gain first insights in a field that is quite new and not understood quite well. Your level of knowledge in this field is low. Explorative research methods help you shed light into the unknown and try to increase your understanding on how things work. Observations of customers, structured interviews, case studies or experimental prototypes are well-proven methods in that space. Validation requires a testable hypothesis that you want to validate (better "falsify"). To phrase a hypothesis, however, you already need deep knowledge in a certain field. Otherwise you will not be able to phrase a hypothesis that eventually can be validated. Surveys or other ways of collecting a vast amount of data with software systems, for instance, are good methods here. 

I always formulate my hypotheses in a way that it is almost impossible to find an experiment to test them. What's wrong? Well, the chance is high that you either don't know enough about the subject and you better explore rather than validate the subject or you are not concrete enough in your thinking. Reduce your scope and boil down your hypotheses (H1) to something more focused (H2 or H3) and try to find a good experiment again. Have a look at the "Levels of Hypotheses"  picture above.

7. Validate Cards

Validate Cards can be used to extract formulated hypotheses from the Hypotheses & Experiments template to plan corresponding experiments in detail and assign team members who are responsible for the execution. The experiment is defined by the method, how you apply it to collect relevant data and how you measure the output of the experiment. Furthermore, a threshold can be defined in terms of "We are convinced if...". This ensures to clarify in advance what result of the experiment is anticipated and how it will be rated. The assigned team member can take the card to do his / her "homework" until the defined due date. Don't forget to plan corresponding activities in the  Action Plan .

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Innovation Footprints

What if . . . ?

A Note on Startup Business Model Hypotheses

March 29, 2015 by Brian Laung Aoaeh

One of the observations I have arrived at over the course of meeting founders of early stage startups is that often it is not clear during our conversations if they have spent time examining the hypotheses that underlie the business model for the startup they are building.

This post ((Any mistakes in quoting from my sources are entirely mine. This post is an updated and adapted version of my posts The Startup Customer Development Model and  Customer Discovery Phase I: State Your Business Model Hypotheses?  which were published at Tekedia.com on September 3rd, 2012 and January 21, 2013 respectively. Large portions of this update are identical to the originals.)) is my attempt to outline some of the areas that I consider as I try to understand an early stage startup’s business model and the hypotheses that are the foundation on which its success must rest. ((I have adapted portions of: Chapter 2 and Chapter 4 of The Startup Owner’s Manual Vol. 1: The Step –by-step Guide for Building a Great Company, Steve Blank and Bob Dorf, Pub. March 2012 by K and S Ranch Publishing Division.))

To ensure we are on the same page, first some definitions;

Definition #1:  What is a startup?  A startup is a temporary organization built to search for the solution to a problem, and in the process to find a repeatable, scalable and profitable business model that is designed for incredibly fast growth. The defining characteristic of a startup is that of experimentation – in order to have a chance of survival every startup has to be good at performing the experiments that are necessary for the discovery of a successful business model. ((I am paraphrasing Steve Blank and Bob Dorf, and the definition they provide in their book  The Startup Owner’s Manual: The Step-by-Step  Guide for Building a Great Company.  I have modified their definition with an element from a discussion in which Paul Graham, founder of Y Combinator discusses the startups that Y Combinator supports.))

Definition #2: What is a business model?  A business model is the description of how a startup will create, deliver and capture value. Alex Osterwalder’s Business Model Canvas is one framework for describing and documenting the elements of a startup’s business model.

Definition #3: What is Customer Development? Customer Development is a 4-step process by which a startup answers the questions it needs to answer in order to find a business model that is repeatable, scalable, and profitable. Step 1 and step 2 of Customer Development cover the “search” phase of a startup’s life-cycle. Step 1 is Customer Discovery. Step 2 is Customer Validation. More on those a little later.

Definition #4 What is a hypothesis?  A hypothesis is a statement, or a group of statements, that proposes an answer to a question, or a solution to a problem, in a manner that is testable through experimentation. The goal of experimentation and testing is to determine if the hypothesis is correct, and to inform the subsequent actions that the startup should take on the basis of that evidence.

Step 1 in the Customer Development Process: Customer Discovery – this involves translating the initial vision behind the startup into a set of hypotheses about each component of the business model. This allows experiments to be performed that either validate or invalidate each proposed hypothesis. In my experience the exercise of testing hypotheses about the business model with prospective customers accomplishes at least two things. First the startup entrepreneur gets to hear directly from customers about the elements of the business model’s value proposition that are most critical from the point of view of the startup’s customers or partners. Second it jump-starts the sales process even before the startup has invested much time or money into building a product. The founders of a hardware startup discussed their idea for an innovative new product with a potential partner. The partner’s input proved crucial in determining the direction they followed with regard to product design – it evolved from a product with one offering to one with three distinct but complementary offerings.

The revenue model also changed based on those discussions. Even better, the partner agreed to work with this startup to bring the product to market when it is ready. Obviously, there’s still a lot to be done – product design, product development and manufacturing for example. Yet those initial discussions have been critical in conferring the kind of credibility that has made it possible for the startup to seek an audience with other potential partners. Customer discovery for this startup also involved market research to determine the priority of features from the perspective of individual end-use customers – the men and women who might actually decide to purchase the startup’s offering once it becomes available to consumers.

Step 2 in the Customer Development Process:  Customer Validation – this step proves that the work done in step 1 is easily repeatable, scalable, and capable of delivering the customer volume required to build a profitable company. The startup I described above is now building prototypes based on all the information it gathered during the Customer Discovery process. Eventually we will test our ability to deploy the product in the field – a few hundred first, then a few thousand, and barring any major setbacks, tens-of-thousands, then hundreds of thousands.

During that process we will test how well the back-end software works with the hardware that we have designed and manufactured once people are actually using the device. At each step I expect we will go back to the drawing board on several aspects of the product and the business model. For example, our pricing model may not reflect reality since our market research confirmed the hypothesis that our potential customers have never encountered a device like the one we are developing. We may discover that customers will gladly pay more for the value proposition we offer than we currently plan to charge. It is important to note that we have gone through a number of product pivots during Customer Discovery. For one, we made an incorrect hypothesis about the amount of space our partners would be willing to devote to this new device, never mind all the assurances they gave us during early conversations.

We also made a number of pivots in terms of the user experience and the interface through which users will interact with the device because we realized that a number of hypotheses we had made about certain design, engineering, and manufacturing issues related to the product were just flat out wrong. The product we will soon show to our partners satisfies the desires individual end-use customers told us they seek in a product like ours ((Market research involved nothing more than a description of the device. In other words, we relied on potential customers’ ability to imagine a future in which they could use the device we were setting out to develop.)), in a manner that accounts for the constraints our partners expressed they would eventually have to contend with in deploying the devices when they come to market. Moreover, this exchange of information led us to develop a product with performance characteristics far superior to what we would have achieved within the parameters of our previous vision. We expect to make a few more pivots before all is said and done.

Developing Hypotheses During Customer Discovery

The first step in customer discovery is developing a rough estimate of market size and sketching an initial business model for your startup using the business model canvas , which I have discussed in some detail in What is Your Business Model?  Using the business model as a guide, develop a hypothesis brief for each component of the business model canvas. A hypothesis brief should contain a succinct statement of the hypothesis itself as well as a sufficiently detailed but brief outline of the information that makes the hypothesis a reasonable and valid one for that business model component.

The market size hypothesis is probably the most critical, even though it does not correspond directly to any of the business model canvas components. Investors like to back companies that target potentially large markets. At the same time, be careful to differentiate the total addressable market opportunity, the serviced addressable market, and your target market. Needless to say, your initial target market will be the smallest of these three. In most cases a bottom-up estimate is better than a top-down estimate because it is relatively easy for an investor who wishes to do so to replicate a bottom-up estimate. Whereas, a top-down estimate could be viewed as “hand-waving” with no basis in reality.

The value proposition hypothesis should discuss the problem your startup solves for its customers. A segment of this brief should capture product features, and a minimum set of initial product features that early customers would be willing to pay for. This is the minimum viable product, a bare-bones version of your product that solves the “core” problem your customers face. Put another way, your minimum viable product is the least developed product that you can create in order to validate your most important hypotheses about the problem you are solving and what your customers or users will accept.

The customer segments hypothesis forces you to answer the questions “Who are my customers?” and “What problems do my customers face?” The hypothesis brief should discuss customer problems, types, and archetypes respectively. Understanding “a day in the life” of your typical customer is a powerful way to understand your startup’s customers. Finally, Steve and Bob suggest you develop a customer influence map. There is an important aspect finding customers that can be overlooked. What is the smallest group of customers that is experiencing the pain or problem you are solving most acutely? Perhaps they do not have enough money to be attractive to incumbents. Or, perhaps they are a niche that is considered weird and unprofitable by your competitors. Start your experiments there. Why? If your product indeed solves their problem, they will adopt it quickly. On the basis of broad adoption within that niche, you can plot a path to other communities of customers who are facing the same problem. In other words, find the groups of people who will be your “Innovators” and “Early Adopters” and focus your early efforts on those groups.

The channels hypothesis should differentiate between physical, web, and mobile channels. An important consideration during the development of this brief is whether your product fits the channel. At this stage it is important to pick the channel with the most potential and to focus on gaining customers and cultivating sales through that channel to the near exclusion of every other alternative. With very few exceptions, since you are still testing your hypotheses, developing your business model, and determining what product is best positioned to solve your customers problems avoid the temptation to launch via multiple channels.

I was having lunch with the founder of an early-stage startup on Thursday, last week. She was giving me an update – the struggle to raise seed capital from investors, what she’s learning about building a team, and so on. We got to talking about how she would distribute her startup’s MVP. Her initial plan would have cost her a lot of money – capital she can’t afford to spend and a significant portion of the round she’s trying to raise, because she was thinking about traditional channels – the most obvious route to the customers she thinks she needs to get to. I pointed out that without further testing, she was taking a very risky gamble whose most likely outcomes do not favor her startup. Instead I suggested she spend the least amount of money she can to test non-traditional channels, and maximize the yield from those avenues before she does anything big and splashy through traditional channels. In this example, her hypothesis was poorly formed because it failed to take her startup’s capital constraints into full consideration.

The market-type and competitive hypothesis discusses the nature of the market into which your startup is entering and tries to anticipate the competitive landscape of the market that you will be attacking. You might consider it the second half of the value proposition hypothesis – your product solves a product for a group of customers, or a market. In broad terms a market already exists, or your startup is creating a completely new market where none existed previously. Your market entry strategy will depend on the market type you identify, as will your cost of entry into that market. In an existing market, your startup will have to position itself against the competition in a manner that ensures it can win given the basis upon which you have chosen to compete.

The customer relationships hypothesis describes how you get, keep and grow your customers. It is similar to the LBGUPS model, which I discussed in What Is Your Business Model?  There’s no need to emphasize that this is an important hypothesis brief – without customers or users your startup will die a not premature death. How you get, grow and keep customers is very channel dependent. Your analysis should take that into account, and should also factor in related costs.

The key resources hypothesis discusses how you’ll obtain resources that are critical to your startup’s operations but that you do not have within the startup. These resources might be physical resources, financial capital, human capital, or intellectual property. In each case it is important to list the resource and an outline of how it will be secured to enable the startup run its operations. For example, servers can be rented in the cloud at a cost that is lower than managing your own server. Another example, a first-time founder who does not yet have a technical co-founder might partner with an outsourced software development shop to build and MVP with which to run some experiments. Often the devshop will remain as a service provider till the startup becomes self-sufficient enough to bring that work in-house. I have a bias for startups that control their intellectual property.

The key-partners hypothesis describes the partners that are essential to enabling your startup to succeed. It also describes the value-exchange that keeps the partnership alive. For example, a startup might have all its development and design work done by a software engineering consulting firm established for that specific purpose. In this case the startup pays the software engineer money in exchange for software engineering related to its product. Key-partner relationships might take the form of a strategic alliance, a joint new business development effort, a key supplier relationship, or co-opetition. Certain of these are more common early in the startup lifecycle, and others are more common late in the startup lifecycle. It is important to realize that a partner should not have control over anything that is critical to your startup’s ability to exist and do business.

The key activities hypothesis summarizes your startup team’s understanding and assumptions about where its energies should be most focused in order to create the most value for its customers. These are those activities that you feel cannot be left to one of your startup’s key partners. For example, a hardware startup might view design as a key activity, while assembly is left to a manufacturing partner in a low-cost manufacturing jurisdiction.

The revenue and pricing hypothesis brief is important because it ensures that the startup can extract value for itself and its investors. It asks a number of simple questions all related to revenue. The nature of the specific questions asked depends on the channel, but the essence of those questions remains the same. Together they should enable you determine if there’s a business worth pursuing along the path you have chosen for your startup.

The cost structure hypothesis brief forms the second half of the value extraction hypotheses – the first being the revenue and pricing hypothesis. Your startup’s cost structure must ensure that it can effectively deliver on the value proposition it has promised customers, and keep a portion of the revenues that the startup cultivates in the form of profits. Here too the questions asked will be relatively simple, and will reflect the channel and the market type. For example, a startup whose only channel is the web will have a lower cost structure than one with a physical channel.

Once your hypothesis briefs are complete, your entire startup team should discuss the output. Seek contradictions, conflicts and inconsistencies. The most important reason for developing these hypotheses is to ensure that the actions that your startup is taking have the highest probability of yielding success that is possible.

During my conversations with founders I listen carefully to determine if the startup has thought about these issues, or is thinking about them – it depends on the stage. I become concerned when I get the sense that important questions have been left unasked and unanswered.

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