A hypothesis is an educated guess about how two or more variables might be related. When conducting scientific research, developing a clear, testable hypothesis is an essential early step. Your hypothesis guides your experiment and helps you determine what data to collect and analyze. While coming up with a good hypothesis takes some thought and research, following some basic guidelines can help you generate a strong, credible hypothesis that is suitable for testing.
The first step to writing a hypothesis is identifying the phenomenon or question you want to investigate. What is the problem or gap in knowledge you are trying to address? You should have a clear sense of the overall issue or topic before formulating a potential hypothesis. For example, if you are interested in plant growth, potential phenomena could relate to the effects of sunlight, water, soil nutrients, temperature, or other variables on various plant characteristics such as height, leaf size, flowering, etc.
Next, do some background research on scientific literature related to your phenomenon of interest. Review previous studies, theories, and findings within your topic area. Understanding what is already known about related processes and potential factors will help you identify key variables or relationships that have not yet been investigated in depth. Your hypothesis should aim to address an open question, knowledge gap, or inconsistency in the existing body of work. Avoid simply restating well-established associations unless you plan to test them under novel conditions.
With your phenomenon and background research in mind, identify the specific variables or factors you want to investigate in your hypothesis. A well-written hypothesis focuses on two or three main variables that have a proposed cause-and-effect relationship. These variables should be reasonably precise and measurable. For example, when formulating a hypothesis related to plant growth you may propose variables such as the amount of sunlight (independent variable) and the height of the plant (dependent variable). Avoid vague or ambiguous terms that would be difficult to operationalize or quantify.
As with identifying variables, it is important to specify the study population or sample your hypothesis will address. Characteristics like the type of plant, animal, or other subjects you will examine need to be defined. For example, you would state whether your sunlight-plant growth hypothesis applies to a specific species of plant rather than all plants generally. Clearly defining your target sample at the outset avoids uncertainty later on.
Now comes the proposition of the potential relationship between your identified variables. This is the core of your hypothesis – your hypothesized association stated concisely and in a testable form. A good hypothesis suggests a causal link that can be supported or refuted by data, often phrased as “if… then…”. For example, your sunlight-plant growth hypothesis may state “If the amount of sunlight a plant receives is increased, then the height of the plant will increase.” Avoid ambiguous or untestable relationships like “is related to” that do not imply a directional causal link.
The next part of a strong hypothesis is explaining the reasoning or basis for the proposed relationship. Why do you think the variables might be associated in the way you stated? Reference relevant theories, previous research findings, or logical inferences that provide justification for investigating this particular relationship. For instance, you could explain that plants need sunlight as an energy source for photosynthesis which enables plant growth processes. Do not present extensive background information – just a concise rationale paragraph.
Your hypothesis should conclude by making a clear, falsifiable prediction about the expected outcome of testing the proposed relationship. In essence, predict what results you anticipate finding if your hypothesis is supported. For example, you may predict that if plants receive more sunlight their average height after one month will be significantly greater than plants receiving less sunlight. A unambiguous, testable prediction is important for setting up objective criteria to evaluate whether your hypothesis is supported or not supported by the data.
Writing your hypothesis following the basic elements described above – phenomenon, variables, population, relationship proposition, reasoning, and prediction – will help ensure it is viable, clear, and appropriate for experimental testing. There are still some aspects of quality to consider. A strong hypothesis should not be obviously true or untestable. It also must be narrow enough to be feasibly studied within time/resource constraints yet broad enough to possibly produce unexpected findings. Consider discussing your hypothesis idea with others to gain feedback on potential limitations or ways to strengthen it before finalizing. Ultimately, developing a solid, evidence-based hypothesis is half the battle of conducting good scientific research.
When you have a draft hypothesis written, review it one more time using the criteria of being specific, measurable, attainable, relevant, and time-bound, commonly referred to with the acronym SMART.
Specific – The hypothesis clearly defines the variables and relationship being investigated without ambiguity.
Measurable – The variables and anticipated results can be concretely observed and quantified.
Attainable – It is feasible to realistically test the hypothesis within resource limitations such as time, equipment, or sample size.
Relevant – The hypothesis addresses a meaningful knowledge gap and furthers scientific understanding of the phenomenon under study.
Time-Bound – The relationships suggested can be reasonably evaluated within the planned study parameters and timeline.
Revising as needed based on these SMART standards will help optimize your hypothesis for guiding an effective research experiment. Finally, be prepared to revisit and potentially refine your hypothesis as necessary during the research process based on unexpected findings or limitations encountered. Hypothesis testing is iterative – re-evaluating and updating hypotheses in response to new evidence is part of the scientific method. With rigorous conceptualization and testing, a strong initial hypothesis can launch an impactful research investigation.
Andrew Stroud