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Pick a statistical test by matching it to your research question, outcome, and study design—not by choosing a familiar option from a software menu. Seven clues help narrow the field: what you want to learn, what you measured, how many groups or conditions you have, whether observations are independent or paired, how many outcomes and predictors are involved, which assumptions apply, and what effect you want to estimate.
1. Start with the question your analysis must answer
Write the question in plain language before naming a test. Are you comparing groups, measuring an association, predicting an outcome, or assessing change over time? The test should address that question, not merely produce a p-value.
Be precise about the quantity of interest—the estimand. For example, “Is the average outcome different between these groups?” and “How strongly are these two variables associated?” are different questions and may require different methods even when they use the same dataset.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →2. Identify the outcome’s measurement scale
The dependent variable—the outcome you want to explain or compare—puts important constraints on candidate methods. Note whether it is numeric, categorical, ordered, or a count, and consider whether its values are continuous or limited to a small set of possibilities. A method appropriate for a numeric outcome may not fit a binary or nominal outcome.
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Also distinguish outcome variables from explanatory variables, sometimes called predictors or independent variables. Their types and roles help determine whether the analysis is a comparison, association test, or model.
3. Count the groups or conditions, and describe their structure
For a group comparison, determine how many groups or conditions are being compared and whether the question concerns a particular contrast or an overall difference. A two-group comparison and a comparison across several groups are not interchangeable setups. When there are several groups, the analysis may need a follow-up plan for locating differences rather than treating one overall test as the whole answer.
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Common examples include a t-test for some numeric-outcome comparisons and ANOVA for some comparisons across multiple groups. These names are starting points, not automatic recommendations: the outcome, design, assumptions, and target comparison still matter.
4. Check whether observations are independent or paired
Independent groups contain different, unrelated observational units. Paired or repeated data arise when the same people or units are measured more than once, or when observations are deliberately matched. Treating repeated measurements as if they came from unrelated people ignores the dependence in the data and can lead to an inappropriate analysis.
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- Independent design: each unit contributes to one group, with no meaningful pairing between groups.
- Paired or repeated design: measurements are linked within a person, unit, or matched pair.
Methods such as Wilcoxon procedures have versions suited to different designs; the name alone does not tell you which version applies. Specify the pairing when selecting and reporting a test.
5. Count outcomes and explanatory variables
A single outcome compared across groups is a simpler structure than a model with several predictors or multiple outcomes. Record how many dependent and independent variables the analysis includes, and whether the question concerns each predictor separately or their combined relationship with the outcome.
A general linear model can accommodate some designs involving multiple explanatory variables and numeric outcomes, but it is not a universal solution. More complex designs may require methods tailored to the outcome type, dependence structure, or other features of the data.
6. Check the assumptions of the specific method
Candidate tests make assumptions about matters such as the data distribution, variance, independence, and measurement scale. Which assumptions matter—and how consequential a violation is—depends on the method and design. Check those assumptions in context rather than choosing a test solely because a normality check did or did not pass.
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Parametric methods such as t-tests, ANOVA, and general linear models are commonly taught alongside nonparametric methods such as chi-square, Wilcoxon, and Mann–Whitney. These are broad examples, not a complete decision tree. Some alternatives may be suitable when assumptions for a particular parametric procedure are not met, but “nonparametric” does not mean assumption-free; the alternative must still fit the outcome and design, and it may answer a different question or have less power in some settings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Decide what result you need to communicate
A test statistic or p-value alone does not describe the size or practical meaning of a result. Plan to report an effect estimate and its uncertainty alongside the test result, using measures that match the question and method. This makes it easier for readers to assess both the direction and magnitude of an observed difference or association.
A practical way to narrow your options
- State the question: comparison, association, prediction, or change.
- Define the outcome: record its scale and whether it is numeric, categorical, ordered, or a count.
- Describe the design: identify the groups or conditions and whether observations are independent, paired, or repeated.
- List the variables: count outcomes and explanatory variables and clarify their roles.
- Choose candidate methods: use the design and outcome to identify plausible options, not just a familiar test name.
- Check fit: review each candidate’s assumptions and confirm it addresses the intended estimand.
- Plan reporting: identify the effect estimate and uncertainty you will report with the test result.
The factors above—distribution, dependence between samples, measurement scale, and the number of dependent and independent variables—are also highlighted in the test-selection chapter excerpt from Scientific Methods for the Humanities (2012), hosted on Scribd. Treat its examples as orientation rather than an authoritative or exhaustive statistical standard.
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