To choose the right statistical test, start with the question your study asks and how its data were collected—not with a software menu or a normality test. Identify the outcome, the groups or conditions, whether observations are independent or paired, and the assumptions of plausible methods. These seven clues narrow the choice without pretending there is one universal decision tree.
1. What question are you trying to answer?
First state the comparison or relationship in plain language. Are you asking whether two groups differ, whether measurements change over time, whether two categorical variables are associated, or whether several predictors relate to an outcome? A test is useful only insofar as it addresses that question. Two analyses of the same dataset can answer different questions, so choose the method to fit the intended conclusion rather than selecting whichever option is easiest to run.
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Be precise about the target of the analysis, too: for example, a difference in means is not the same target as a difference in ranks or a relationship between categories. This helps distinguish tests that may look interchangeable in software but do not answer precisely the same thing.
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2. What kind of outcome did you measure?
Identify the outcome variable—the result you want to explain or compare—and its measurement scale. A numeric measurement, an ordered rating, and a category such as yes/no call for different approaches. Do not decide from labels alone: consider what the values represent and what summary or contrast would be meaningful.
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The number of outcomes matters as well. A design with one outcome differs from one that analyzes several outcomes, and the method should match how those outcomes are defined and whether they are considered together.
3. How many groups or conditions are being compared?
Count the groups or conditions relevant to the question. A comparison between two groups is not the same design as a comparison among several groups. The number of groups helps narrow candidate methods, but it does not settle the choice: the outcome scale, dependence structure, and assumptions still matter.
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For numeric outcomes, a t-test and ANOVA are familiar examples for certain group-comparison settings. They are not universal choices for every two-group or multi-group problem; the design and assumptions determine whether either is appropriate.
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4. Are the observations independent, paired, or repeated?
Ask whether each observation comes from a different, unrelated unit or whether measurements are linked. Two groups of unrelated participants are independent; measurements from the same participants before and after an intervention are paired. Repeated measurements from the same unit also require accounting for that dependence.
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Treating linked measurements as independent discards part of the design and can make the analysis unsuitable. When observations are paired or repeated, choose a method that represents that structure rather than simply counting the number of columns or groups in the dataset.
5. How many predictors are in the design?
List the explanatory variables—the predictors or factors that may account for differences in the outcome—and note how each is measured. A single group indicator is simpler than a design with several predictors. With multiple predictors or outcomes, a general linear model may be a candidate for some numeric-outcome questions, but the model’s form and assumptions must fit the data and the question.
Keep the outcome and predictors distinct: the outcome is what is being explained; predictors are the variables used to explain or compare it. This inventory prevents you from choosing a test intended for a simpler design than the one you actually have.
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6. Which assumptions does the candidate method require?
Check the assumptions of the specific procedure you are considering. Depending on the method, relevant issues may include the distribution of the data or model residuals, whether observations are independent, and whether variances are sufficiently similar. A normality result by itself does not choose the test: it says nothing about whether the design is paired, what the outcome represents, or whether the method answers the intended question.
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Nonparametric procedures can be useful alternatives in some settings where assumptions such as normality or roughly equal variances are not met. Examples include Wilcoxon and Mann–Whitney procedures. But “nonparametric” does not mean assumption-free, and an alternative may answer a different question or have less power in a particular setting. Confirm that its assumptions and interpretation fit your design rather than switching methods automatically after one diagnostic result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. What result do you need to interpret and report?
Before settling on a method, ask what conclusion the reader should be able to draw: a difference between groups, an association between categories, or a relationship between predictors and an outcome. Make sure the method’s result corresponds to that conclusion. A test’s p-value alone does not convey the size or practical meaning of an effect; report an effect estimate and its uncertainty alongside the test result for a more informative analysis.
A practical way to narrow the options
- Write the question: specify the comparison or relationship you want to assess.
- Describe the data: identify the outcome’s measurement scale, number of outcomes, groups or conditions, and explanatory variables.
- Map the design: determine whether observations are independent, paired, or repeated.
- Choose plausible candidates: use the question, outcome, and design to identify methods worth considering; t-test, ANOVA, general linear model, chi-square, Wilcoxon, and Mann–Whitney are examples, not an exhaustive menu.
- Check assumptions and interpretation: verify the candidate’s requirements and confirm that its result answers the question you wrote.
- Plan the report: present the effect estimate and uncertainty as well as the test result.
As a broad orientation, t-tests and ANOVA are examples of parametric methods, while chi-square, Wilcoxon, and Mann–Whitney are examples often described as nonparametric. These labels alone do not determine which procedure is correct: the specific outcome, design, assumptions, and target of the analysis do.
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