MONITORING, EVALUATION AND LEARNING
Office of Trade and Regulatory Reform
Bureau of Economic Growth, Education, and Environment
US Agency for International Development (USAID)
For more information, please contact .
Have you read my two bestsellers, Unlearn and Lean Enterprise? If not, please do. If you have, please write a review!
Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.
We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving, or abandoning any hypothesis as we made further observations from the results we achieved.
Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.
Practicing Hypothesis-Driven Development [1] is thinking about the development of new ideas, products, and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.
We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.
We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behavior in the environment or market it is developed for.
The key outcome of an experimental approach is measurable evidence and learning. Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?
In order to learn we need to use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.
As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.
The steps of the scientific method are to:
We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative and can leverage well-understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses. Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed. Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.
The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.
As A…. <role>
I Want… <goal/desire>
So That… <receive benefit>
Behaviour Driven Development (BDD) and Feature Injection aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.
In Order To… <receive benefit>
As A… <role>
When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce the bias of interpretation of results.
If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.
Therefore, a user story structure to support Hypothesis-Driven Development would be;
What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.
What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?
What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.
The threshold you use for statistical significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.
For example, if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.
The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate, and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?
Hypotheses, when aligned to your MVP, can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.
Business story.
We Believe That increasing the size of hotel images on the booking page Will Result In improved customer engagement and conversion We Will Have Confidence To Proceed When we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.
It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise, we are essentially blind to the outcomes of our efforts.
In agile software development, we define working software as the primary measure of progress. By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.
Ideally, we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.
Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behavior. Alternative testings options can be customer surveys, paper prototypes, user and/or guerilla testing.
One example of a company we have worked with that uses Hypothesis-Driven Development is lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared the story of how they improved conversion by 400% within a week.
Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing costs, leaving our competitors in the dust. Ideally, we can achieve the ideal of one-piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.
As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is before you work on the solution.
We also run a workshop to help teams implement Hypothesis-Driven Development . Get in touch to run it at your company.
[1] Hypothesis-Driven Development By Jeffrey L. Taylor
A discounted way to invest in the future, scaling the heights of human performance with annastiina hintsa, the ceo of hintsa performance, how high performance organizations innovate at scale, read my newsletter.
Insights in every edition. News you can use. No spam, ever. Read the latest edition
We've just sent you your first email. Go check it out!
Chapter 6 overview.
This chapter discusses the third step of the SGAM, highlighted below in gold, Hypothesis Development.
A hypothesis is often defined as an educated guess because it is informed by what you already know about a topic. This step in the process is to identify all hypotheses that merit detailed examination, keeping in mind that there is a distinction between the hypothesis generation and hypothesis evaluation .
If the analysis does not begin with the correct hypothesis, it is unlikely to get the correct answer. Psychological research into how people go about generating hypotheses shows that people are actually rather poor at thinking of all the possibilities. Therefore, at the hypothesis generation stage, it is wise to bring together a group of analysts with different backgrounds and perspectives for a brainstorming session. Brainstorming in a group stimulates the imagination and usually brings out possibilities that individual members of the group had not thought of. Experience shows that initial discussion in the group elicits every possibility, no matter how remote, before judging likelihood or feasibility. Only when all the possibilities are on the table, is the focus on judging them and selecting the hypotheses to be examined in greater detail in subsequent analysis.
When screening out the seemingly improbable hypotheses, it is necessary to distinguish hypotheses that appear to be disproved (i.e., improbable) from those that are simply unproven. For an unproven hypothesis, there is no evidence that it is correct. For a disproved hypothesis, there is positive evidence that it is wrong. Early rejection of unproven, but not disproved, hypotheses biases the analysis, because one does not then look for the evidence that might support them. Unproven hypotheses should be kept alive until they can be disproved. One example of a hypothesis that often falls into this unproven but not disproved category is the hypothesis that an opponent is trying to deceive us. You may reject the possibility of denial and deception because you see no evidence of it, but rejection is not justified under these circumstances. If deception is planned well and properly implemented, one should not expect to find evidence of it readily at hand. The possibility should not be rejected until it is disproved, or, at least, until after a systematic search for evidence has been made, and none has been found.
There is no "correct" number of hypotheses to be considered. The number depends upon the nature of the analytical problem and how advanced you are in the analysis of it. As a general rule, the greater your level of uncertainty, or the greater the impact of your conclusion, the more alternatives you may wish to consider. More than seven hypotheses may be unmanageable; if there are this many alternatives, it may be advisable to group several of them together for your initial cut at the analysis.
Developing good hypotheses requires divergent thinking to ensure that all hypotheses are considered. It also requires convergent thinking to ensure that redundant and irrational hypotheses are eliminated. A hypothesis is stated as an "if … then" statement. There are two important qualities about a hypothesis expressed as an "if … then" statement. These are:
Hypothesis development is ultimately experience-based. In this experienced-based reasoning, new knowledge is compared to previous knowledge. New knowledge is added to this internal knowledge base. Before long, an analyst has developed an internal set of spatial rules. These rules are then used to develop possible hypotheses.
Developing hypotheses and evidence is the beginning of the sensemaking and Analysis of Competing Hypotheses (ACH) process. ACH is a general purpose intelligence analysis methodology developed by Richards Heuer while he was an analyst at the Central Intelligence Agency (CIA). ACH draws on the scientific method, cognitive psychology, and decision analysis. ACH became widely available when the CIA published Heuer’s The Psychology of Intelligence Analysis . The ACH methodology can help the geospatial analyst overcome cognitive biases common to analysis in national security, law enforcement, and competitive intelligence. ACH forces analysts to disprove hypotheses rather than jump to conclusions and permit biases and mindsets to determine the outcome. ACH is a very logical step-by-step process that has been incorporated into our Structured Geospatial Analytical Method. A complete discussion of ACH is found in Chapter 8 of Heuer’s book.
Science follows at least three general methods of problem solving using hypotheses. These can be called the:
The first two are the most popular but they can lead to overlooking relevant perspectives, data, and encourage biases. It has been suggested that multiple hypotheses offers a more effective way of overcoming this problem.
Our desire to reach an explanation commonly leads us to a tentative interpretation that is based on a single case. The explanation can blind us to other possibilities that we ignored at first glance. This premature explanation can become a ruling theory, and our research becomes focused on proving that ruling theory. The result is a bias to evidence that disproves the ruling theory or supports an alternate explanation. Only if the original hypothesis was by chance correct does our analysis lead to any meaningful intelligence work. The working hypothesis is supposed to be a hypothesis to be tested, not in order to prove the hypothesis, but as a stimulus for study and fact-finding. Nonetheless, the single working hypothesis can become a ruling theory, and the desire to prove the working hypothesis, despite evidence to the contrary, can become as strong as the desire to prove the ruling theory.
The method of multiple working hypotheses involves the development, prior to our search for evidence, of several hypotheses that might explain what are attempting to explain. Many of these hypotheses should be contradictory, so that many will prove to be improbable. However, the development of multiple hypotheses prior to the intelligence analysis lets us avoid the trap of the ruling hypothesis and thus makes it more likely that our intelligence work will lead to meaningful results. We open-mindedly envision all the possible explanations of the events, including the possibility that none of the hypotheses are plausible and the possibility that more research and hypothesis development is needed. The method of multiple working hypotheses has several other beneficial effects on intelligence analysis. Human actions are often the result of several factors, not just one, and multiple hypotheses make it more likely that we will see the interaction of the several factors. The beginning with multiple hypotheses also promotes much greater thoroughness than analysis directed toward one hypothesis, leading to analytic lines that we might otherwise overlook, and thus to evidence and insights that might never have been considered. Thirdly, the method makes us much more likely to see the imperfections in our understanding and thus to avoid the pitfall of accepting weak or flawed evidence for one hypothesis when another provides a more possible explanation.
Multiple hypotheses have drawbacks. One is that it is difficult to express multiple hypotheses simultaneously, and therefore there is a natural tendency to favor one. Another problem is developing a large number of hypotheses that can be tested. A third possible problem is that of the indecision that arises as an analyst balances the evidence for various hypotheses, which is likely preferable to the premature rush to a false conclusion.
Action 1: Brainstorming . Begin with a brainstorming session with your knowledge team to identify a set of alternative hypotheses. Focus on the hypotheses that are:
State the hypotheses stated in an "if ... then" format, for example:
Action 2: Review the hypotheses for testability , i.e., can evidence be could found to test the validity of the statement.
Action 3: Check the hypotheses for falsifiability , i.e., could evidence reveal that such an idea is not true.
Action 4: Combine redundant hypotheses.
Action 5:Consider the elimination of improbable and unproven hypotheses.
One of the greatest fears of product managers is to create an app that flopped because it's based on untested assumptions. After successfully launching more than 20 products, we're convinced that we've found the right approach for hypothesis-driven development.
In this guide, I'll show you how we validated the hypotheses to ensure that the apps met the users' expectations and needs.
Hypothesis-driven development is a prototype methodology that allows product designers to develop, test, and rebuild a product until it’s acceptable by the users. It is an iterative measure that explores assumptions defined during the project and attempts to validate it with users’ feedbacks.
What you have assumed during the initial stage of development may not be valid for the users. Even if they are backed by historical data, user behaviors can be affected by specific audiences and other factors. Hypothesis-driven development removes these uncertainties as the project progresses.
For us, the hypothesis-driven approach provides a structured way to consolidate ideas and build hypotheses based on objective criteria. It’s also less costly to test the prototype before production.
Using this approach has reliably allowed us to identify what, how, and in which order should the testing be done. It gives us a deep understanding of how we prioritise the features, how it’s connected to the business goals and desired user outcomes.
We’re also able to track and compare the desired and real outcomes of developing the features.
Our success in building apps that are well-accepted by users is based on the Lean UX definition of hypothesis. We believe that the business outcome will be achieved if the user’s outcome is fulfilled for the particular feature.
Here’s the process flow:
How Might We technique → Dot voting (based on estimated/assumptive impact) → converting into a hypothesis → define testing methodology (research method + success/fail criteria) → impact effort scale for prioritizing → test, learn, repeat.
Once the hypothesis is proven right, the feature is escalated into the development track for UI design and development.
Whether it’s the initial stage of the project or after the launch, there are always uncertainties or ideas to further improve the existing product. In order to move forward, you’ll need to turn the ideas into structured hypotheses where they can be tested prior to production.
To start with, jot the ideas or assumptions down on paper or a sticky note.
Then, you’ll want to widen the scope of the questions and assumptions into possible solutions. The How Might We (HMW) technique is handy in rephrasing the statements into questions that facilitate brainstorming.
For example, if you have a social media app with a low number of users, asking, “How might we increase the number of users for the app?” makes brainstorming easier.
Once you’ve got a list of questions, it’s time to decide which are potentially more impactful for the product. The Dot Vote method, where team members are given dots to place on the questions, helps prioritize the questions and assumptions.
Our team uses this method when we’re faced with many ideas and need to eliminate some of them. We started by grouping similar ideas and use 3-5 dots to vote. At the end of the process, we’ll have the preliminary data on the possible impact and our team’s interest in developing certain features.
This method allows us to prioritize the statements derived from the HMW technique and we’re only converting the top ones.
The questions lead to a brainstorming session where the answers become hypotheses for the product. The hypothesis is meant to create a framework that allows the questions and solutions to be defined clearly for validation.
Our team followed a specific format in forming hypotheses. We structured the statement as follow:
We believe we will achieve [ business outcome],
If [ the persona],
Solve their need in [ user outcome] using [feature].
Here’s a hypothesis we’ve created:
We believe we will achieve DAU=100 if Mike (our proto persona) solve their need in recording and sharing videos instantaneously using our camera and cloud storage .
It’s crucial to validate each of the assumptions made on the product features. Based on the hypotheses, experiments in the form of interviews, surveys, usability testing, and so forth are created to determine if the assumptions are aligned with reality.
Each of the methods provides some level of confidence. Therefore, you don’t want to be 100% reliant on a particular method as it’s based on a sample of users.
It’s important to choose a research method that allows validation to be done with minimal effort. Even though hypotheses validation provides a degree of confidence, not all assumptions can be tested and there could be a margin of error in data obtained as the test is conducted on a sample of people.
The experiments are designed in such a way that feedback can be compared with the predicted outcome. Only validated hypotheses are brought forward for development.
Testing all the hypotheses can be tedious. To be more efficient, you can use the impact effort scale. This method allows you to focus on hypotheses that are potentially high value and easy to validate.
You can also work on hypotheses that deliver high impact but require high effort. Ignore those that require high impact but low impact and keep hypotheses with low impact and effort into the backlog.
At Uptech, we assign each hypothesis with clear testing criteria. We rank the hypothesis with a binary ‘task success’ and subjective ‘effort on task’ where the latter is scored from 1 to 10.
While we’re conducting the test, we also collect qualitative data such as the users' feedback. We have a habit of segregation the feedback into pros, cons and neutral with color-coded stickers. (red - cons, green -pros, blue- neutral).
The best practice is to test each hypothesis at least on 5 users.
The hypothesis-driven approach is not a single-ended process. Often, you’ll find that some of the hypotheses are proven to be false. Rather than be disheartened, you should use the data gathered to finetune the hypothesis and design a better experiment in the next phase.
Treat the entire cycle as a learning process where you’ll better understand the product and the customers.
We’ve found the process helpful when developing an MVP for Carbon Club, an environmental startup in the UK. The app allows users to donate to charity based on the carbon-footprint produced.
In order to calculate the carbon footprint, we’re weighing the options of
Upon validation, we’ve found that all of the users opted for the second option as they are concerned about linking an unknown app to their banking account.
The result makes us shelves the first assumption we’ve made during pre-Sprint research. It also saves our client $50,000, and a few months of work as connecting the app to the bank account requires a huge effort.
Once you’ve got the confidence that the remaining hypotheses are validated, it’s time to develop the product. However, testing must be continued even after the product is launched.
You should be on your toes as customers’ demands, market trends, local economics, and other conditions may require some features to evolve.
If there’s anything that you could pick from our experience, it’s these 5 points.
1. Should every idea go straight into the backlog? No, unless they are validated with substantial evidence.
2. While it’s hard to define business outcomes with specific metrics and desired values, you should do it anyway. Try to be as specific as possible, and avoid general terms. Give your best effort and adjust as you receive new data.
3. Get all product teams involved as the best ideas are born from collaboration.
4. Start with a plan consists of 2 main parameters, i.e., criteria of success and research methods. Besides qualitative insights, you need to set objective criteria to determine if a test is successful. Use the Test Card to validate the assumptions strategically.
5. The methodology that we’ve recommended in this article works not only for products. We’ve applied it at the end of 2019 for setting the strategic goals of the company and end up with robust results, engaged and aligned team.
You'll have a better idea of which features would lead to a successful product with hypothesis-driven development. Rather than vague assumptions, the consolidated data from users will provide a clear direction for your development team.
As for the hypotheses that don't make the cut, improvise, re-test, and leverage for future upgrades.
Keep failing with product launches? I'll be happy to point you in the right direction. Drop me a message here.
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.
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 .
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.
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 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 🙂
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.
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.
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
Hi” best wishes to you and your very nice blog”
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .
Hypotheses connect theory to data and guide the research process towards expanding scientific understanding
Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.
Alternative hypothesis.
The research hypothesis is often called the alternative or experimental hypothesis in experimental research.
It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.
The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).
A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:
In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.
It states that the results are not due to chance and are significant in supporting the theory being investigated.
The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.
The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.
It states results are due to chance and are not significant in supporting the idea being investigated.
The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.
Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.
This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.
A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.
It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.
For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.
A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)
It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.
For example, “Exercise increases weight loss” is a directional hypothesis.
The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.
Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.
It means that there should exist some potential evidence or experiment that could prove the proposition false.
However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.
For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.
Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.
All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.
In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.
We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.
If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.
Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.
Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).
Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
There are 5 main steps in hypothesis testing:
Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.
Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.
After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.
The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.
For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.
There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).
If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.
Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.
Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .
Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.
In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.
In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).
Discover proofreading & editing
The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .
In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.
In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.
However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.
If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”
These are superficial differences; you can see that they mean the same thing.
You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.
If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Methodology
Research bias
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 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).
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.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved September 8, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/
Other students also liked, choosing the right statistical test | types & examples, understanding p values | definition and examples, what is your plagiarism score.
If...,Then...
Angela Lumsden/Getty Images
A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.
In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."
In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.
Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.
Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.
In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.
For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."
An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.
But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."
In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.
Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.
Examples of a hypothesis include:
Our editors will review what you’ve submitted and determine whether to revise the article.
scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .
The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).
Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.
The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.
Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).
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
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).
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).
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:
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.
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.
Make sure that the hypothesis clearly defines the topic and the focus of the experiment.
Follow this template: If a specific action is taken, then a certain outcome is expected.
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.
Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.
The types of research hypothesis are stated below:
It predicts the relationship between a single dependent variable and a single independent variable.
It predicts the relationship between two or more independent and dependent variables.
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.
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.
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.
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.
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 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.
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:
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.
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.
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
It awesome. It has really positioned me in my research project
Brief and easily digested
Rate this article Cancel Reply
Your email address will not be published.
In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…
The process of choosing the right research design can put ourselves at the crossroads of…
The COPE forum discussion held in December 2023 initiated with a fundamental question — is…
Embarking on your first academic conference experience? Fear not, we got you covered! Academic conferences…
Research Recommendations – Guiding policy-makers for evidence-based decision making
Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of…
Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…
Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…
How to Design Effective Research Questionnaires for Robust Findings
Sign-up to read more
Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:
We hate spam too. We promise to protect your privacy and never spam you.
Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.
Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton
Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.
Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.
A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.
Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.
But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.
Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.
The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.
As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).
When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.
To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.
There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.
A coherent explanation or interpretation of one or more phenomena.
A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.
A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.
The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.
Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
IMAGES
VIDEO
COMMENTS
How to Write a Strong Hypothesis | Steps & Examples
Research Hypothesis: Definition, Types, Examples and ...
Theories and Hypotheses. Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena.Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions ...
Research Hypothesis: What It Is, Types How to Develop?
Hypothesis: Definition, Examples, and Types
Practicing Hypothesis-Driven Development is thinking about the development of new ideas, products and services - even organizational change - as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.
What is a research hypothesis: How to write it, types, and ...
A development hypothesis states what will occur if a particular intervention is undertaken, or a combination of several building blocks that are critical for bringing about a particular development outcome are put in place. USAID's CDCS guidance makes it clear that these propositions cannot simply be wishes. USAID expects development hypotheses to be grounded in evidence.
Practicing Hypothesis-Driven Development[1] is thinking about the development of new ideas, products, and services - even organizational change - as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.
Hypothesis development is ultimately experience-based. In this experienced-based reasoning, new knowledge is compared to previous knowledge. New knowledge is added to this internal knowledge base. Before long, an analyst has developed an internal set of spatial rules. These rules are then used to develop possible hypotheses.
Hypothesis-driven development is a prototype methodology that allows product designers to develop, test, and rebuild a product until it's acceptable by the users. It is an iterative measure that explores assumptions defined during the project and attempts to validate it with users' feedbacks.
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 ...
Hypothesis-driven development offers a structured approach to problem-solving and innovation while minimizing risks, enabling evidence-based decisions, and fostering experimentation. This series includes the benefits of a hypothesis-drive approach for Scrum Teams and product delivery as well as templates and examples of different hypothesis formats.
What Is A Research Hypothesis? A Simple Definition
DEVELOPING HYPOTHESES & RESEARCH QUESTIONS Definitions of hypothesis "It is a tentative prediction about the nature of the relationship between two or more variables." "A hypothesis can be defined as a tentative explanation of the research problem, a possible outcome of the research, or an educated guess about the
Research Hypothesis In Psychology: Types, & Examples
Hypothesis Testing | A Step-by-Step Guide with Easy ...
What Is a Hypothesis? (Science) - Scientific Method
Scientific hypothesis | Definition, Formulation, & Example
How to Develop a Good Research Hypothesis
4. Formulate your hypothesis. After collecting background information and making a prediction based on your question, plan a statement that lays out your variables, subjects and predicted outcome. Whether you write it as an "if/then" or declarative statement, your hypothesis should include the prediction to be tested.
Developing a Hypothesis Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton. ... So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger ...
Scientific Hypothesis | Development & Examples - Lesson
What Is a Hypothesis? (With Types, Examples and FAQS)
Just-world fallacy