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How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

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

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

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

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

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

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

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

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

 Statistics

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

Research bias

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

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

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

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

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The Craft of Writing a Strong Hypothesis

Deeptanshu D

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types-of-hypotheses

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-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.

market hypothesis in research

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How to Develop a Good Research Hypothesis

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

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

Table of Contents

What is Hypothesis?

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

What is a Research Hypothesis?

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

Characteristics of a Good Research Hypothesis

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

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

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

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

research hypothesis example

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

Source: Educational Hub

How to formulate a research hypothesis.

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

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

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

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

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

3. Define the variables

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

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

4. Scrutinize the hypothesis

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

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

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

2. Complex Hypothesis

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

3. Directional Hypothesis

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

4. Non-directional Hypothesis

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

5. Associative and Causal Hypothesis

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

6. Null Hypothesis

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

7. Alternative Hypothesis

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

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).
Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

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

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

Importance of a Testable Hypothesis

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

Thanks a lot for your valuable guidance.

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

Useful piece!

This is awesome.Wow.

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

Nicely explained

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

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

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

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Efficient market hypothesis (emh): definition and critique.

market hypothesis in research

Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT).

market hypothesis in research

What Is the Efficient Market Hypothesis (EMH)?

The efficient market hypothesis (EMH), alternatively known as the efficient market theory, is a hypothesis that states that share prices reflect all available information and consistent alpha generation is impossible.

According to the EMH, stocks always trade at their fair value on exchanges, making it impossible for investors to purchase undervalued stocks or sell stocks for inflated prices. Therefore, it should be impossible to outperform the overall market through expert stock selection or market timing , and the only way an investor can obtain higher returns is by purchasing riskier investments.

Key Takeaways

  • The efficient market hypothesis (EMH) or theory states that share prices reflect all information.
  • The EMH hypothesizes that stocks trade at their fair market value on exchanges.
  • Proponents of EMH posit that investors benefit from investing in a low-cost, passive portfolio.
  • Opponents of EMH believe that it is possible to beat the market and that stocks can deviate from their fair market values.

Investopedia / Theresa Chiechi

Understanding the Efficient Market Hypothesis (EMH)

Although it is a cornerstone of modern financial theory, the EMH is highly controversial and often disputed. Believers argue it is pointless to search for undervalued stocks or to try to predict trends in the market through either fundamental or technical analysis .

Theoretically, neither technical nor fundamental analysis can produce risk-adjusted excess returns (alpha) consistently, and only inside information can result in outsized risk-adjusted returns.

$599,090.00

The February 9, 2024 share price of the most expensive stock in the world: Berkshire Hathaway Inc. Class A (BRK.A).

While academics point to a large body of evidence in support of EMH, an equal amount of dissension also exists. For example, investors such as Warren Buffett have consistently beaten the market over long periods, which by definition is impossible according to the EMH.

Detractors of the EMH also point to events such as the 1987 stock market crash, when the Dow Jones Industrial Average (DJIA) fell by over 20% in a single day, and asset bubbles as evidence that stock prices can seriously deviate from their fair values.

The assumption that markets are efficient is a cornerstone of modern financial economics—one that has come under question in practice.

Proponents of the Efficient Market Hypothesis conclude that, because of the randomness of the market, investors could do better by investing in a low-cost, passive portfolio.

Data compiled by Morningstar Inc., in its June 2019 Active/Passive Barometer study, supports the EMH. Morningstar compared active managers’ returns in all categories against a composite made of related index funds and exchange-traded funds (ETFs) . The study found that over a 10 year period beginning June 2009, only 23% of active managers were able to outperform their passive peers. Better success rates were found in foreign equity funds and bond funds. Lower success rates were found in US large-cap funds. In general, investors have fared better by investing in low-cost index funds or ETFs.

While a percentage of active managers do outperform passive funds at some point, the challenge for investors is being able to identify which ones will do so over the long term. Less than 25 percent of the top-performing active managers can consistently outperform their passive manager counterparts over time.

What Does It Mean for Markets to Be Efficient?

Market efficiency refers to how well prices reflect all available information. The efficient markets hypothesis (EMH) argues that markets are efficient, leaving no room to make excess profits by investing since everything is already fairly and accurately priced. This implies that there is little hope of beating the market, although you can match market returns through passive index investing.

Has the Efficient Markets Hypothesis Any Validity?

The validity of the EMH has been questioned on both theoretical and empirical grounds. There are investors who have beaten the market, such as  Warren Buffett , whose investment strategy focused on  undervalued  stocks made billions and set an example for numerous followers. There are portfolio managers who have better track records than others, and there are investment houses with more renowned research analysis than others. EMH proponents, however, argue that those who outperform the market do so not out of skill but out of luck, due to the laws of probability: at any given time in a market with a large number of actors, some will outperform the mean, while others will  underperform .

Can Markets Be Inefficient?

There are certainly some markets that are less efficient than others. An inefficient market is one in which an asset's prices do not accurately reflect its true value, which may occur for several reasons. Market inefficiencies may exist due to information asymmetries, a lack of buyers and sellers (i.e. low liquidity ), high transaction costs or delays, market psychology, and human emotion, among other reasons. Inefficiencies often lead to  deadweight losses . In reality, most markets do display some level of inefficiencies, and in the extreme case, an inefficient market can be an example of a  market failure .

Accepting the EMH in its purest ( strong ) form may be difficult as it states that all information in a  market , whether public or private, is accounted for in a stock's price. However, modifications of EMH exist to reflect the degree to which it can be applied to markets:

  • Semi-strong efficiency : This form of EMH implies all public (but not non-public ) information is calculated into a stock's current share price. Neither fundamental nor  technical analysis  can be used to achieve superior gains.
  • Weak efficiency : This type of EMH claims that all past prices of a stock are reflected in today's stock price. Therefore, technical analysis cannot be used to predict and beat the market.

What Can Make a Market More Efficient?

The more participants are engaged in a market, the more efficient it will become as more people compete and bring more and different types of information to bear on the price. As markets become more active and liquid, arbitrageurs will also emerge, profiting by correcting small inefficiencies whenever they might arise and quickly restoring efficiency.

The Library of Economics and Liberty. " Efficient Capital Markets ."

Yahoo Finance. " Berkshire Hathaway Inc. (BRK-A) ."

Federal Reserve History. " Stock Market Crash of 1987 ."

Morningstar. " Active Funds vs. Passive Funds: Which Fund Types Had Increased Success Rates? "

market hypothesis in research

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Grad Coach

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?

market hypothesis in research

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

market hypothesis in research

Psst… there’s more (for free)

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

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Research limitations vs delimitations

15 Comments

Lynnet Chikwaikwai

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

Dr. WuodArek

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

Afshin

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

GANDI Benjamin

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

Lucile Dossou-Yovo

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

Pereria

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

Egya Salihu

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

Mulugeta Tefera

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

Derek Jansen

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

Samia

could you please elaborate it more

Patricia Nyawir

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

Hopeson Khondiwa

This is very helpful

Dr. Andarge

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

TAUNO

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?

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LESSWRONG LW

The efficient market hypothesis in research.

A classic economics joke goes like this:

Two economists are walking down a road, when one of them notices a $20 bill on the ground. He turns to his friend and exclaims: "Look, a $20 bill!" The other replies: "Nah, if there's a $20 on the bill on the ground, someone would've picked it up already."

The economists in the joke believe in the Efficient Market Hypothesis (EMH) , which roughly says that financial markets are efficient and there's no way to "beat the market" by making intelligent trades.

If the EMH was true, then why is there still a trillion-dollar finance industry with active mutual funds and hedge funds? In reality, the EMH is not a universal law of economics (like the law of gravity), but more like an approximation. There may exist inefficiencies in markets where stock prices follow a predictable pattern and there is profit to be made (e.g.: stock prices fall when it's cloudy in New York). However, as soon as someone notices the pattern and starts exploiting it (by making a trading algorithm based on weather data), the inefficiency disappears. The next person will find zero correlation between weather in New York and stock prices.

market hypothesis in research

There is a close parallel in academic research. Here, the "market" is generally efficient: most problems that are solvable are already solved. There are still "inefficiencies": open problems that can be reasonably solved, and one "exploits" them by solving it and publishing a paper. Once exploited, it is no longer available: nobody else can publish the same paper solving the same problem.

Where does this leave the EMH? In my view, the EMH is a useful approximation, but its accuracy depends on your skill and expertise. For non-experts, the EMH is pretty much universally true: it's unlikely that you've found an inefficiency that everyone else has missed. For experts, the EMH is less often true: when you're working in highly specialized areas that only a handful of people understand, you begin to notice more inefficiencies that are still unexploited.

A large inefficiency is like a $20 bill on the ground: it gets picked up very quickly. An example of this is when a new tool is invented that can straightforwardly be applied to a wide range of problems. When the BERT model was released in 2018, breaking the state-of-the-art on all the NLP benchmarks, there was instantly an explosion of activity as researchers raced to apply it to all the important NLP problems and be the first to publish. By mid-2019, all the straightforward applications of BERT were done, and the $20 bill was no more.

market hypothesis in research

Above: Representation of the EMH in research. To outsiders, there are no inefficiencies; to experts, inefficiencies exist briefly before they are exploited. Loosely inspired by this diagram by Matt Might.

The EMH implies various heuristics that I use to guide my daily research. If I have a research idea that's relatively obvious, and the tools to attack it have existed for a while (say, >= 3 years), then probably one of the following is true:

  • Someone already published it 3 years ago.
  • Your idea doesn't work very well.
  • The result is not that useful or interesting.
  • One of your basic assumptions is wrong, so your idea doesn't even make sense.

Conversely, a research idea is much more likely to be fruitful (i.e., a true inefficiency) if the tools to solve it have only existed for a few months, requires data and resources that nobody else has access to, or requires rare combinations of insights that conceivably nobody has thought of.

Outside the realm of the known (the red area in my diagram), there are many questions that are unanswerable. These include the hard problems of consciousness and free will, P=NP, etc, or more mundane problems where our current methods are not strong enough. For an outsider, these might seem like inefficiencies, but it would be wise to assume they're not. The EMH ensures that true inefficiencies are quickly picked up.

To give a more relatable example, take the apps Uber (launched in 2009) and Instagram (launched in 2010). Many of the apps on your phone probably launched around the same time. In order for Uber and Instagram to work, people needed to have smartphones that were connected to the internet, with GPS (for Uber) and decent quality cameras (for Instagram). Neither of these ideas would've been possible in 2005, but thanks to the EMH, as soon as smartphone adoption took off, we didn't have to wait very long to see all the viable use-cases for the new technology to emerge.

Originally posted on my blog: Lucky's Notes .

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The research community is very far from being efficient.

One of my own fields of research is Markov chain Monte Carlo methods, and their applications in computations for Bayesian models.  Markov chain Monte Carlo (MCMC) was invented in the early 1950s, for use in statistical physics.  It was not used by Bayesian statisticians until around 1990. There was no reason that it could not have been used before then - the methods of the 1950s could have been directly applied to many Bayesian inference problems.

In 1970, a paper generalizing the most common MCMC algorithm (the "Metropolis" method) was published in Biometrika, one of the top statistics journals.  This didn't prompt anyone to start using it for Bayesian inference.

In the early 1980s, MCMC was used by some engineers and computer scientists (eg, by Geoffrey Hinton for maximum likelihood inference for log-linear models with latent variables, also known as "Boltzmann machines").  This also didn't prompt anyone to start using it for Bayesian inference.  

After a form of MCMC starting being used by Bayeian statisticians around 1990, it took many years for the literature on MCMC methods used by physicists to actually be used by statisticians.  This despite the fact that I wrote a review paper describing just about all these methods in terms readily accessible to statisticians in 1993.

In 1992, I started using the Hamiltonian Monte Carlo method (aka, hybrid Monte Carlo, or HMC)  for Bayesian inference for neural network models.   This method was invented by physicists in 1987.  (It could have been invented in the 1950s, but just wasn't.)  I demonstrated that HMC was often hundreds or thousands of times faster than simpler methods, gave talks on this at conferences, and wrote my thesis (later  book) on Bayesian learning in which this was a major theme.  It wasn't much used by other statisticians until after I wrote another review paper in 2010, which for some reason led to it catching on.  It is now widely used in packages such as Stan.

Another of my research areas is error-correcting codes.  In 1948, Claude Shannon proved his noisy coding theorem, establishing the theoretical (but not practical) limits of error correction. In 1963, Robert Gallager invented Low Density Parity Check (LDPC) codes.  For many years after this, standard texbooks stated that the theoretical limit proved to be possible by Shannon was unlikely to ever be closely approached by codes with practical encoding and decoding algorithms.  In 1996, David MacKay and I showed that a slight variation on Gallager's LDPC codes comes very close to achieving the Shannon limit on performance.  (A few years before then, "Turbo codes" had achieved similar performance.)  These and related codes are now very widely used. 

These are examples of good ideas that took far longer to be widely used than one would expect in an efficient research community.  There are also many bad ideas that persist for far longer than they should.

I think both problems are at least partially the result of perverse incentives of researchers. 

Lots of research is very incremental - what you describe as "...there was instantly an explosion of activity as researchers raced to apply it to all the important NLP problems and be the first to publish".  Sometimes, of course, this explosion of activity is useful.  But often it is not - the idea isn't actually very good, it's just the sort of idea on which it is easy to write more and more papers, often precisely because it isn't very good.  And sometimes this explosion of activity doesn't happen when it would have been useful, because the activity required is not the sort that leads to easy papers - eg, the needed activity is to apply the idea to practical problems, but that isn't the "novel" research that leads to tenure, or the idea requires learning some new tools and that's too much trouble, or the way forward is by messy empirical work that doesn't look as impressive as proving theorems (even if the theorems are actually pointless), or extending an idea that someone else came up with doesn't seem like as good a career move as developing your own ideas (even when your ideas aren't as good).

The easy rewards from incremental research may mean that researchers don't spend much, or any, time on thinking about actual original ideas.  Getting such ideas may require reading extensively in diverse fields, and getting one's hands dirty with the low-level work that is necessary to develop real intuition about how things work, and what is important. Academic researchers can't easily find time for this, and may be forced into either doing incremental research, or becoming research managers rather than actual researchers.  

In my case, the best research environment was when I was a PhD student (with Geoffrey Hinton).  But I'm not sure things are still as good for PhD students.  The level of competition for short-term rewards may be higher than back in the 1990s.

Yeah, I agree that the EMH holds true more for incremental research than for truly groundbreaking ideas. I'm not too familiar with MCMC or Bayesian inference so correct me if I'm wrong, but I'm guessing these advancements required combining of ideas that nobody expected would work? The deep learning revolution could probably have happened sooner (in the sense that all the prerequisite tools existed), but few people before 2010 expected neural networks to work so consequently the inefficiencies there remained undiscovered.

At the same time, I wouldn't denigrate research that you might view as "incremental", because most research is of that nature. By this I mean, for every paper published in the ACL / EMNLP conferences, if the authors hadn't published it, someone else would almost certainly have published something very similar within 1-2 years. Exceptions to this are few and far between -- science advances via an accumulation of many small contributions.

I think the problem with MCMC is that's an incredible dirty thing from the perspective of a mathematician. It's way to practically useful as opposed to being about mathematical theorems. MCMC is about doing an efficient way to do calculation and doing calculations is low status for mathematicians. It has ugly randomness in it.

I was personally taught MCMC when studying bioinformatics and I was a bit surprised when talking with a friend who was a math Phd who had a problem where MCMC would have worked very well for a subproblem but it was completely out his radar. 

MCMC was something that came out of compuer science and not out of the statistic community. Most people in statistics cared about statistical significance. The math community already looks down at the statistics community and MCMC seems to be even worse from that perspective.

My statistics proof said that in principle bioinformatics could have had been a subfield of statistics but the way of doing things was in the beginning rejected by the statistics community so that bioinformatics had to become it's own field (and it's the field where MCMC was used a lot because you actually need it for the problems that bioinformatics cares about). 

Certainly some incremental research is very useful.  But much of it isn't.  I'm not familiar with the ACL and EMNLP conferences, but for ML and statistics, there are large numbers of papers that don't really contribute much (and these aren't failed attempts at breakthroughs).  You can see that this must be true from the sheer volume of papers now - there can't possibly be that many actual advances.

For LDPC codes, it certainly was true that for years people didn't realize their potential.  But there wasn't any good reason not to investigate - it's sort of like nobody pointing a telescope at Saturn because Venus turned out to be rather featureless, and why would Saturn be different?  There was a bit of tunnel vision, with an unjustified belief that one couldn't really expect much more than what the codes being investigated delivered - though one could of course publish lots of papers on a new variation in sequential decoding of convolutional codes. (There was good evidence that this would never lead to the Shannon limit - but that of course must surely be unobtainable...)

Regarding MCMC and Bayesian inference, I think there was just nobody making the connection - nobody who actually knew what the methods from physics could do, and also knew what the computational obstacles for Bayesian inference were.  I don't think anyone thought of applying the Metropolis algorithm to Bayesian inference and then said, "but surely that wouldn't work...".  It's obviously worth a try.

There is a close parallel in academic research. Here, the "market" is generally efficient: most problems that are solvable are already solved. 

That's false. For most problem that are solvable nobody cares about solving them as it's not valuable to solve them.

Larry McEnerney's LEADERSHIP LAB: The Craft of Writing Effectively has a huge impact on how I think about reasearch. The research that's done depends highly on what's valued in certain research communities. 

Two look at two of my recent research questions:

After reading about how Fibrin fixates fascia because it's in fascial fluid and accumlates when the flow of fascial fluid is blocked, I wanted to know the protein composition of fascial fluid. I don't think there's any available paper that gives me the answer. That's not because it would be very hard to do the relevant research but because few people care about the protein composition of fascial fluid. This is not research I can do, but someone who had a lab could easily do it.

My other recent research idea came after reading Core Pathways of Aging . I had a feeling like "Why did I believe the bullshit they told my in MicroBiology 101 (might also have been 102) about transposons?" Then I started running an evolutionary simulation of human hunter gatherers for transposons to understand better what's going on. While I searched around for papers on other simulations on hunter gatherers and found that nobody really does this and that people instead use some differential equations from the 60's with fancy math from a time before the compute to do good simulations was available. 

While I'm still in my project and the outcome isn't yet final, it's very interesting how my model produces right now the 51% male birth rate that's the literature value and not the 50% I would have naively expected. It seems to me like the research topic is underexplored because it didn't fall into what any researcher in the field considered interesting.

True, I guess a more precise statement is "most problems that are important and solvable are already solved". There are lots of small gaps in my research as well, like "what if we make a minor adjustment to method X" -- whatever the outcome, it's below the bar for a publication so they're generally left untouched.

True, I guess a more precise statement is "most problems that are important and solvable are already solved".

No, there are plenty of important problems that nobody has an incentive to solve. See Eliezer's Inadequate Equilibria . It's central that there's a research community that cares about the problem.

Take Ivermectin pre-COVID. It worked very well for getting rid of parasites after being invented in 1975. Well enough to lead to a Nobel prize. In 2018 there's a paper saying:

Ivermectin has long been clinically administered for the treatment of parasitosis ( 63 ), but has recently come to attention as a potential inhibitor of IMPα/β ( 64 ). Ivermectin inhibition of IMPα/β has shown to inhibit the replication of RNA viruses such as dengue virus and HIV-1 ( 64 ). Ivermectin was recently tested for the inhibition of IAV in vitro , with nuclear import of vRNP complex (of both wild-type and antiviral MxA escape mutant) efficiently inhibited ( 65 ). Given ivermectin’s longstanding record of clinical applications and FDA-approved status, repurposing of this drug for the treatment of IAV should be considered, especially while under threat of pandemic IAV outbreak.

The question whether Ivermectin is a viable treatment against influenza and maybe a broad spectrum antiviral is an important problem. On the other hand it's not a very valuable problem for anyone to find out given that Ivermectin is long off patent. 

The way the last sentence of the paper is formulate is very interesting. As far as Influenza being important, the fact that we have every year a lot of influenza deaths should be enough to demonstrate that it's an important problem. The community that produces regular drugs however doesn't really care about repurposing a generic. 

On the other hand there's a community that cares for pandemic preparedness. The pandemic preparedness community cares less about whether it's possible to patent treatments and cares more about health outcomes so he pitches it as being valuable for the pandemic preparedness community. 

The tools to solve the problem of whether or not you can use ivermectin as antiviral against influenza and also against Coronaviruses exist since it hit the market in 1981. It was just never valuable enough for anybody to find out until some people thought about running small trials for all the substances that might help against COVID-19.

The people who did consider it valuable also were mostly small funders so we still haven't highly powered trials that tell us with high certainty about the effects of ivermectin. The big healthcare funders didn't consider it valuable to fund the studies early in the pandemic but that doesn't mean running the studies wasn't important.

MIRI's attempt to publish ideas into the academic community had the problem of there not really being an existing academic community that values what they do. That doesn't mean that MIRI's work is not important. It just means nobody in academia cares.

Important work that has no field that values it has a hard time getting produced.

To be fair, almost nobody considered a pandemic to be a serious possibility prior to 2020, so it is understandable that pandemic preparedness research was a low-priority area. There may be lots of open and answerable questions in unpopular topics, but if the topic is obscure, the payoff for making a discovery is small (in terms of reputation and recognition).

Of course, COVID-19 has proven to us that pandemic research is important, and immediately researchers poured in from everywhere to work on various facets of the problem (e.g., I even joined in an effort to build a ventilator simulator). The payoff increased, so the inefficiencies quickly disappeared.

Now you can argue that pandemic research should've been more prioritized before. That is obvious in hindsight but was not at all obvious in 2019. Out of the zillions of low-priority research areas that nobody cares about now, how will you decide which one will become important? Unless you have a time machine to see into the future, it remains a low-payoff endeavor.

In the US alone depending on the year there are something between 10,000 and 60,000 flu deaths and a lot of additional harm due to people being ill. Whether or not pandemics are a concern it's an important problem to deal with that. 

There was money in pandemic preparedness. The Gates Foundation and organizations like CEPI were interested in it. They let themselves be conned by mRNA researchers and as a result funded mRNA research where there's a good chance that it had net harm as it made us focus our vaccine trials on mRNA vaccines instead of focusing them on well-understand existing vaccine platforms that are easy to scale up and come with less side-effects. 

The study from 2018 I referred is written in a way it is to advocate that part of this money goes into studying ivermectin for influenza. With the knowledge of hindsight that would have been more important.

In any case, my main point here is that what was prioritized (or was found to be valuable in Larry McEnerney terms) and what was important were two different things. 

If you want to do important research and not just research that's prioritized (found to be valuable by a particular community) it's important to be able to mentally distinguish the two. Paradigm changing research for example generally isn't valuable for the community that operates in an existing paradigm.

Sydney Brenner who was for example on of the people who started the molecular biology field is on record for saying that the kind of paradigm creating work back then would have been a hard time getting funded in today's enviroment. 

Given that there's an efficient market as far as producing work that's valued by established funders and not an efficient market for creating important work any researcher that actually wants to do important work and not just work that's perceived as valuable has to keep the two apart. The efficient market hypothesis implies that most of the open opportunities to do important work are not seen as valuable by existing research communities. 

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9 Key stages in your marketing research process

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

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

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

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

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

The marketing research process – an overview

A typical marketing research process is as follows:

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

Step 1: Defining the marketing research problem

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

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

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

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

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

Step 2: Developing a research program: method of inquiry

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

The scientific method includes the following steps:

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

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

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

Step 3: Developing a research program: research method

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

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

Step 4: Developing a research program: research design

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

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

Exploratory marketing research

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

Descriptive marketing research

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

Causal marketing research

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

Step 5: Choose your sample

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

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

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

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

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

Step 6: Gather data

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

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

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

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

  • Social media ( polls and listening )

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

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

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

Step 7: Analysis and interpretation

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

Statistical tests and data processing tools can reveal:

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

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

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

Here are some of the most common tools:

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

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

Step 8: The marketing research results

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

The results will include:

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

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

Traditionally, you would prepare two written reports:

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

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

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

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

Step 9 Turn your insights into action

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

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

Download now: 8 Innovations to Modernize Market Research

Scott Smith

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

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