One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.
Developing a solid, testable hypothesis is one of the most important steps when designing an experiment or starting a research project But it can also be one of the trickiest parts of the scientific method In this comprehensive guide, we break down everything you need to know to craft a strong hypothesis.
What Exactly is a Hypothesis?
A hypothesis is an educated guess or prediction about the relationship between two or more variables. It’s an attempt to answer your research question before you begin experimenting or collecting and analyzing data.
Some key things to know about hypotheses
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A hypothesis establishes a tentative explanation that can be tested through further research and experimentation
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It connects the broad, overarching research question you wish to answer with the specific variables you will measure.
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The hypothesis proposes a specific, testable prediction about the outcome of your experiment, rather than just stating an observation or opinion.
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It allows you to make clear the relationship you are studying (such as correlation or causation) between variables.
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The hypothesis must be falsifiable, meaning it can be disproven by your research results.
In short, your hypothesis turns your curiosity about the world into a statement you can test empirically. It’s your best initial guess at what you think the answer to your research question might be.
Types of Hypotheses
There are a few different forms a hypothesis can take:
Simple Hypothesis
This states the predicted relationship between two variables – one independent and one dependent.
Example: Regular exercise leads to weight loss.
Complex Hypothesis
This proposes a relationship between more than two variables – for example, two independent and one dependent.
Example: People who eat diets high in fat and sugar AND get little physical activity are more likely to be overweight.
Directional Hypothesis
This predicts the specific nature of the relationship between variables.
Example: Young children have faster reaction times than elderly adults.
Non-Directional Hypothesis
This only proposes that a relationship exists, without specifying details about that relationship.
Example: Reaction time differs between young children and elderly adults.
Null Hypothesis
This predicts that no relationship exists between the variables under study. It is the default position that there is no effect or association.
Example: There is no correlation between hours studied and exam scores.
Alternative Hypothesis
This directly contrasts with the null hypothesis to offer the counter explanation that a real relationship does exist.
Example: There is a positive correlation between hours studied and exam scores.
What Makes a Good Hypothesis?
While hypotheses come in various forms, a solid, testable hypothesis has several key characteristics:
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Cause and effect: The hypothesis should suggest a causal relationship – that changes in one variable produce changes in another.
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Falsifiability: A good hypothesis must be capable of being disproven by the results of research and testing.
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Testability: The variables in the hypothesis must be measurable so you can practically test the prediction.
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Specificity: It identifies the particular variables being studied and the population being examined.
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Scope: A good hypothesis is focused on testing a specific relationship between a limited set of variables. It avoids sweeping generalizations.
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Simplicity: The hypothesis should be stated as clearly and concisely as possible in plain language.
Let’s look at each of these criteria more closely:
Cause and Effect
Hypotheses propose that one thing affects another in a predictable way. There is a clear relationship where the independent, manipulated variable is presumed to influence the dependent, responding variable.
So make sure your hypothesis suggests causation rather than just correlation. Don’t just say “There is a relationship…” but rather “There is a causal relationship…”
Falsifiability
A good hypothesis must be constructed in a way that allows it to be disproven if your experimental results end up not supporting it. This is a fundamental requirement of the scientific method – if no evidence could possibly disprove your hypothesis, then testing it is pointless.
So avoid hypotheses that cannot be definitively refuted, no matter the outcome. The hypothesis must make a specific prediction that’s capable of being proven false by your research.
Testability
To test your hypothesis, you need to be able to practically measure and observe the variables involved. So those variables need to be specific and concrete enough to collect usable data on.
If your variables are too broad or abstract, look for ways to narrowly define and operationalize them into quantifiable indicators. Make sure your hypothesis deals with things that can be directly measured and analyzed in your study.
Specificity
Your hypothesis should identify the particular variables being related or compared, and the specific population those variables are being observed in.
Don’t just say “Students who study hard generally get better grades.” Name the exact variables like GPAs, study hours per week, or test scores. And specify the population, like “freshman college students taking introduction biology…”
Scope
The hypothesis should be reasonably narrow in scope – focused on testing the relationship between a limited set of clearly defined variables. Avoid broad, sweeping hypotheses that try to make very general predictions.
Instead of saying “Pesticide use has far-reaching impacts on the natural environment,” design a more specific, limited hypothesis like “Pesticide X reduces insect diversity in 5-acre farming plots.”
Simplicity
Write your hypothesis in clear, plain, and concise language. Avoid confusing jargon or unnecessary complexity. The reader should immediately understand the variables involved and the relationship you are proposing between them.
If possible, phrase it in an “if…then…” statement expressing the causal relationship. For example, “If media portrayals normalize substance abuse (independent variable), then rates of adolescent drug use go up (dependent variable).”
How to Write a Hypothesis Step-by-Step
Now let’s walk through the key steps involved in developing a testable hypothesis:
Step 1: Ask a Research Question
Every hypothesis starts with a broad research issue you want to explore. This gives you direction and context for forming your hypothesis. Some examples:
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Do study skills courses improve academic performance in college freshmen?
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How does the introduction of non-native species affect local ecosystem diversity?
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What design factors make websites more usable for elderly people?
Make your research question as focused and specific as possible, while still allowing for investigation through research.
Step 2: Do Background Reading
Conduct some preliminary research and review existing studies related to your topic. This will provide context for narrowing your research question and developing an informed hypothesis grounded in established theory and knowledge.
Look for previous similar studies and note their findings and limitations. Identify gaps that your own research could potentially fill.
Step 3: Specify Your Variables
Identify the specific independent and dependent variables you want to examine. The independent variable is the one you deliberately manipulate or change to test its effects on the dependent variable, which you simply measure and observe.
For example, with the research question “Do study skills courses improve academic performance in college freshmen?” the independent variable is completing a study skills course and the dependent variable is academic performance as measured through GPAs.
Step 4: Formulate a Testable Hypothesis
Using your specified variables, construct a hypothesis stating the expected relationship between them. Make sure your prediction meets the falsifiability, testability, and scope criteria outlined earlier.
Example hypothesis: “Completing a 16-week study skills course will increase first-year college students’ GPA in their second semester compared to students who do not take the course.”
Step 5: Determine Your Methodology
Decide what research methods you will use to collect data relevant to evaluating your hypothesis – whether quantitative, qualitative, mixed, experimental, correlational, etc.
Outline your plan for controlling variables, ensuring valid measurement, selecting your subjects, gathering data, and analyzing results. Your methodology should produce data capable of confirming or denying your hypothesis.
Step 6: Conduct Your Study and Collect Data
Carry out your experiment or investigation according to the methodology you developed. Systematically record your measurements and observations and document everything you do. Stick closely to your data collection plan to maximize the validity of your findings.
Step 7: Analyze and Report Your Results
Once you’ve completed gathering data, perform appropriate statistical analyses to determine what your results reveal about your hypothesis. Organize and present your data clearly, and report whether your findings confirm or disprove your original prediction. Discuss the implications in relation to existing research and theories.
This rigorous hypothesis testing process allows you to draw valid, evidence-based conclusions from your study. And even if your hypothesis turns out to be wrong, reporting on failed hypotheses is still useful for advancing scientific knowledge!
Examples of Good Hypotheses
Let’s look at a few examples of well-written hypotheses across different fields:
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Psychology: First-year college students who attend time-management seminars will achieve higher first-semester GPAs than those who do not.
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Healthcare: Hospital patients who receive daily 15-minute massages experience less postoperative pain compared to patients receiving standard care.
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Marketing: Coffee shops that play slow
Research Question vs Hypothesis
It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”
A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.
What is a Hypothesis in Research?
Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.
6 Steps to Formulate a STRONG Hypothesis | Scribbr
What is an example of a hypothesis?
It’s essentially an educated guess—based on observations—of what the results of your experiment or research will be. Some hypothesis examples include: If I water plants daily they will grow faster. Adults can more accurately guess the temperature than children can. Butterflies prefer white flowers to orange ones.
What makes a good hypothesis?
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). Hypotheses propose a relationship between two or more types of variables.
What is a hypothesis in science?
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. Daily apple consumption leads to fewer doctor’s visits. What is a hypothesis? What is a hypothesis?
How do you develop a hypothesis?
Developing a hypothesis is easy. Most research studies have two or more variables in the hypothesis, particularly studies involving correlational and experimental research. The researcher can control or change the independent variable (s) while measuring and observing the independent variable (s). “How long a student sleeps affects test scores.”
