How to Write a Hypothesis: Types, 4 Steps and Examples
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. For example, a study designed to examine the relationship between sleep deprivation and test performance might have a useful scientific hypothesis: “This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived.”
This section of your report or proposal aims to define what you are testing in your research precisely. It’s also important to clearly state how you went about your research and why you chose this particular method over others.
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A hypothesis describes a natural pattern or an explanation of some real-world phenomenon that you can test through observation and experimentation. The most common way a hypothesis is used in scientific research is as a tentative, testable, and falsifiable statement that explains some observed phenomenon in nature. Suppose enough evidence accumulates to support a useful hypothesis. In that case, it moves to the next step — known as a theory — in the scientific method and becomes accepted as a valid explanation of a phenomenon.
The concept of the hypothesis is extremely important to science because it provides an expected outcome that you can test by observing nature. Without hypotheses, scientists would not know what they should look for when making observations or finding patterns among existing data.
Characteristics of a Hypothesis
Now that you have an idea of what a hypothesis is and how it’s written, let’s talk about the characteristics and primary features of a scientific hypothesis:
- A hypothesis is a statement. In other words, the scientist experimenting will be able to state their hypothesis in a single sentence.
- A hypothesis is testable. The scientist must be able to experiment to test whether or not their prediction was accurate.
- A hypothesis is measurable. The scientist must be able to measure whatever phenomenon they want to prove, disprove or predict using tools available within her field of study (for example, a biologist might want to measure saltwater levels in different parts of an ocean).
- A hypothesis is falsifiable. This means there should also be some evidence to convince scientists that the initial idea was incorrect (the null hypothesis). This is also referred to as the falsification principle or the likelihood of the null hypothesis.
- A hypothesis is logical. In other words, it makes sense based on known facts and common sense reasoning.
- A hypothesis is useful. Scientists must consider whether their prediction can affect change in their world (or help them better understand their world). This doesn’t mean that there has to be some practical application for the prediction; it might just mean increasing understanding of how something works.
- Hypotheses are based on observations that raise questions and lead scientists to seek answers.
Sources of a Hypothesis
A good hypothesis can be derived from information gathered in several ways. A good source is well-known and trustworthy. If you visit many websites, you may want to check if the author or creator of the site is well known and respected in their field, as this will likely mean that they are publishing good research. Another way to find a good source is to look for publications from well-known institutions, such as universities.
If you have found a credible source, it’s important to make sure it’s up-to-date. In general, articles made recently are more likely to contain accurate information than those made many years ago (in scientific fields where research progresses rapidly). Older sources may not account for new technologies or innovations that have changed our understanding of research topics.
One major factor that determines whether a source is reliable or not is objectivity—the extent to which the article presents only facts and avoids personal opinions on the subject at hand. An objective study will come solely from data collected by researchers and will not be influenced by any other factors (such as social opinion). Don’t pick outdated or biased sources; these usually aren’t good choices for your hypothesis.
Types of Hypothesis
The following are the numerous types of hypotheses that can be employed when seeking to prove a new theory:
- Null Hypothesis – The null hypothesis is sometimes called the “no difference” hypothesis. The null hypothesis is good for experimentation because it’s simple to disprove. If you disprove a null hypothesis, that is evidence of a relationship between the variables you are examining. For example, if you use a t-test to analyze your scientific data and the results show a significant difference, you disprove the null hypothesis.
- Alternative Hypothesis – An alternative hypothesis is exactly what it sounds like—it’s an alternative to the null hypothesis. In other words, it’s what we believe will happen in an experiment or study based on prior research or knowledge in the field. When using inferential statistics, we often use a statistical test to determine whether our alternative hypothesis is correct or not.
- Directional vs. non-directional hypotheses – A directional research hypothesis specifies the direction of its relationship between variables (i.e., predictors and outcomes). In contrast, non-directional research hypotheses do not specify this directionality (i.e., specify that there will be some relationship).
- Simple vs. complex hypotheses – A simple research problem might have one independent variable (predictor) and one dependent variable (outcome) tested in an experiment. In contrast, a complex research problem might have multiple independent variables (predictors) tested with multiple dependent variables (outcomes). Simple hypotheses tend only to be used by undergraduate researchers who are still learning statistics and experimental design methods. However, scientists rarely use them in practice today due to their low power for detecting relationships among variables. Professional researchers more commonly test complex hypotheses because they tend to have more power for detecting relationships among variables than simple ones do—and thus reject Type II errors with greater frequency than when using simple ones. Thus, if there truly is an effect present in your data.
- Intervening hypothesis – this type of statement is also called a casual hypothesis. This means a causal relationship between two variables, with one variable causing the effect on another. You might include an intervening variable in this scenario. The researcher will identify the two variables and the direction of the relationship between them. In other words, what causes what?
- The statistical hypothesis involves testing if there is a significant difference between two or more groups on a continuous dependent variable. A common example will be to test if students from different years differ in their average exam results.
- Theoretical hypothesis – making an educated guess about how things work based on available theoretical or empirical evidence or using logic or intuition. This type of statement tends to be used when not much is known about an area and research is needed to develop it further. For instance, there may be little information available at the start of most projects, so it’s necessary to make a basic assumption or develop basic statements about what you think may happen before building upon those ideas through research and analysis later in your project.
- Research Hypothesis – when writing up any research study, it’s important to include a section where you state your hypotheses so that they can either be supported or refuted by your findings; this enables you to move forward with further studies around these areas as part of building upon existing knowledge within your field of interest.
Hypothesis vs. Prediction
For the science fair, the notion of the scientific hypothesis is typically an educated prediction about the relationship between two variables. A scientific hypothesis is a statement of what you think will happen in a scientific investigation, and it is usually very specific.
A prediction is a statement that anticipates whether something will be true or false in the future. It may or may not be based on research data or knowledge. In other words, it’s an analysis of potential outcomes based on research and data. For example, if you anticipate rain tomorrow morning, that’s a prediction.
While you can test a hypothesis through scientific experimentation, many types of predictions are difficult to verify scientifically. For example, someone might predict that they’ll win the lottery this weekend—but there’s no way to test if their prediction is accurate until the drawing takes place! However, the absence of proof doesn’t mean they’re wrong; they could still strike it rich!
How to Write a Hypothesis
Now that you’re done with the theory of your study, it’s time to write up your hypothesis. It is first necessary for you to define what you intend to do with the data. Answering this question will allow you to determine exactly how and where you will collect the data and what questions or theories you’re interested in answering through your findings.
In this section, we will guide you through the main stages of writing a good hypothesis and provide handy tips and examples to help you overcome this challenge:
1. Define Your Research Question
Spend some time thinking about your research question. Don’t rush it! If you jump into your hypothesis without sufficient background research, you risk steering your paper off on a tangent. You must test everything around a specific, focused question that you want to answer. You can think of this as the center of the target—your hypothesis will revolve around answering this question.
Below is an example of a good research question:
How do different types and intensities of light affect plant growth?
2. Conduct Your Basic Initial Research
You have a question, so now it’s time to do a little digging. The research you conduct before writing your hypothesis will depend on your topic of interest, but here are some general guidelines for how to proceed:
- Find out what other researchers have discovered about the topic. By reading books, journal articles, and encyclopedia entries about your subject area, you can do this. This will help you understand what is currently known and the unanswered questions that remain in your field of study.
- Talk to an expert. Ask a professor in your field what questions they are most interested in studying right now. They should be able to give you more specific guidance on which areas need more research and where there is room for further exploration within them
- Observe the real world around you and come up with new ideas! Sometimes inspiration strikes when you least expect it, so take notes on any interesting thoughts that enter your mind throughout the day
3. Formulate a Hypothesis
A hypothesis is an informed prediction or explanation that you can test using a scientific method and tools. You want to make sure your hypothesis will be testable by the scientific method you choose. Also, keep in mind that a valid experiment requires only one variable to change at a time. You need to state your formal hypothesis as an “If/Then” statement:
If (I do this), then (this will happen)
Write down your theory as an “if…then” statement. This gives you something specific to work with and helps guide the planning of your experiment. For example, if I add fertilizer, my plant will grow faster than plants in unfertilized soil.
4. Refine Your Hypothesis
Now that you’ve done some research and developed a hypothesis, it’s time to test it. As the researcher, you need to know how to write a hypothesis because it will guide your investigation. Be sure to read the article above for more information on a hypothesis, how you write one, and examples of hypotheses.
As part of this process, you should also be able to identify independent and dependent variables in your experiment. The independent variable is the factor manipulated during an experiment; it changes the outcome of an experiment. In contrast, dependent variables are observations related to or depend on independent variables within the experiment (they might change as a result). This distinction allows researchers to test their theories or hypotheses by designing experiments that focus on cause-and-effect relationships between different components of a system or phenomenon being studied.
Try writing out your hypothesis in as many different ways as possible until you find one that captures exactly what question(s) about the topic you want to answer with your research project.
Research Question Examples:
- How does the gender of a teacher affect student success?
- How do exercise and diet affect heart health in children?
- How does age affect alcohol abuse?
- Does garlic improve health? Does an apple a day keep the doctor away?
- Do violent video games lead to aggressive behavior in children?
Hypothesis Example: If we give students a chance to learn important life skills, they will be more motivated to succeed.
Null Hypothesis Examples: There is no difference between the learning abilities of boys and girls. Garlic does not affect improving health.
To sum up, your hypothesis is a prediction you make based on your research. It will help you focus on the right questions and issues as you conduct your research, collect and analyze your data, and interpret and report your findings. When it comes time to design experiments to test your hypothesis, you will be able to use this information to determine the best course of action forward.
Get Help from the Experts to Make a Perfect Hypothesis for You!
If you need help making a perfect hypothesis, do not hesitate to get help from the experts. Getting assistance from the experts is a great option. If you don’t have enough time and knowledge to make a perfect hypothesis, it will be better if you get help from the experts. The experts are professional and qualified in their respective fields, so they can easily make a perfect hypothesis for you.
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