Choose a statistical test by starting with the question you want to answer and the study design—not by checking whether the data look normal. Identify the outcome and predictors, determine whether observations are independent or paired, and then choose a method whose assumptions and estimate match your target. The guide below helps narrow common options; specialized designs may need a statistician or a discipline-specific method guide.
What statistical test should I use?
First decide what you want the analysis to establish. Are you estimating a difference, testing an association, predicting an outcome, comparing a distribution with a reference, or describing data? If you intend to make an inferential claim, write the null and alternative hypotheses before selecting a procedure. Planning the question and possible analyses before collecting data can prevent a mismatch between the study and the analysis; the R Handbook’s guide to choosing a statistical test discusses this planning step.
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Next, identify the variables and the structure of the observations. UCLA’s guide to statistical analyses using R organizes common choices around variable type and distribution. The distinction between independent and related observations is equally important: a before-and-after measurement on the same participant is not two independent samples. A guide to variables and commonly used statistical designs describes how design and variable types inform procedure choice.
A short decision sequence
- State the target. Specify the difference, association, prediction, or distributional feature you want to estimate or test.
- Classify the variables. Distinguish categorical (nominal), ordinal, and continuous interval or ratio variables. For a continuous outcome, say whether the target is a mean or another feature of its distribution.
- Describe the design. Record how many groups or predictors there are and whether observations are independent, matched, paired, clustered, or repeated over time.
- Choose a method family. Use the question and design to narrow candidates, rather than treating a list of test names as an automatic answer.
- Check assumptions and reporting needs. Consider whether the method’s assumptions fit the actual design and whether its output will provide the estimate, uncertainty, and effect-size information needed to answer the question.
Which test fits common research questions?
These are common starting points, not an exhaustive inventory. The named procedures target different questions; they are not interchangeable just because software offers them.
#1 Best Overall
| Question and design | Common starting point | Key choice or caution |
|---|---|---|
| Is a continuous sample mean different from a reference value? | One-sample t test | State the reference and target mean; assess whether the design and assumptions fit. |
| Do two independent groups differ on a continuous outcome? | Independent-samples t test | Consider Welch’s version when equal variances are not justified. |
| Did the same participants change between two measurements? | Paired t test | Preserve the within-person pairing in the analysis. |
| Do three or more groups differ on a continuous outcome? | One-way ANOVA | Define planned contrasts or follow-up comparisons; a regression model may better express a question involving covariates. |
| Are two categorical variables associated? | Chi-square test of association | Check whether the table and design support the approximation; sparse tables may need another procedure. |
| Is a yes/no outcome related to one or more predictors? | Logistic regression | Distinguish prediction from causal claims and account for design and confounding. |
| Are two continuous variables associated? | Correlation or regression | Correlation describes strength and direction of association; regression models an outcome from predictors and can adjust for other predictors. |
| Is the outcome ordinal, or is a rank-based target appropriate? | Ordinal model or rank-based procedure | Select according to the target and design, not merely because a normality check failed. |
How do variable types and design change the choice?
Continuous outcomes and group comparisons
A t test or ANOVA is a common mean-comparison approach when the outcome, design, and assumptions support it. For two independent groups, Welch’s t test avoids requiring the equal-variance assumption of the conventional pooled-variance t test. For three or more groups, ANOVA can test an overall difference, but that result does not by itself identify which groups differ. Plan contrasts or suitable follow-up comparisons rather than running repeated unplanned tests without considering the inference problem. If the question includes multiple predictors, covariate adjustment, or prediction, regression may state the target more directly.
Paired, repeated, clustered, or matched observations
When measurements come from the same people or matched units, preserve that relationship in the analysis. Treating paired observations as independent discards part of the design and can lead to an unsuitable test. Repeated observations over time and clustered data also require methods that account for their dependence; a basic independent-samples test may not do so.
Rank #2
- This guide is a perfect overview for the topics covered in introductory statistics courses.
Categorical and binary outcomes
A chi-square test is a common table-based test of association between two categorical variables. Do not choose it merely because measurements have been coded as numbers: the values must represent categories for this use. If a table is sparse, consider a suitable alternative instead of relying automatically on the chi-square approximation. No single expected-cell cutoff is universal across all designs and procedures, so use guidance specific to the table and analysis.
For a binary outcome modeled against one or more predictors, logistic regression is a common model family. It can estimate associations or support prediction, but a fitted model does not establish causation on its own. The design and treatment of potential confounding determine whether a causal interpretation is defensible.
Rank #3
Ordinal outcomes and distributional alternatives
Ordinal responses have an order but not necessarily equal spacing between categories. Depending on the question and design, an ordinal regression model, rank-based method, permutation test, or another robust procedure may be appropriate. These options answer different questions and are not interchangeable. The R Handbook discusses several alternatives for ordinal outcomes and cases where standard assumptions do not fit.
Which assumptions should I check?
Check assumptions against the study design and the method’s target, rather than applying a generic normality rule. Normality assumptions often concern model errors or residuals—not a requirement that every raw variable appear normally distributed. UCLA’s statistical-analysis guide flags this distinction.
- Independence or pairing: Confirm that the procedure matches whether observations are independent, matched, repeated, or clustered.
- Outcome scale: Verify that the outcome is continuous, categorical, binary, or ordinal in the sense required by the method.
- Distribution and residuals: Examine the relevant distributional conditions for the method, including residual behavior where applicable.
- Variance structure: Check whether an equal-variance assumption is reasonable for the comparison; if not, consider a method such as Welch’s t test when it fits the question.
- Model form: For regression, assess whether the specified relationship between outcome and predictors is credible and whether adjustment variables are appropriate.
Unequal variance, non-normality, repeated measures, clustering, confounding, and sparse categorical data do not all call for the same fix. “Nonparametric” is not a universal replacement category: select an alternative that preserves the target and design.
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A test statistic or p value is not the complete research answer. Report the estimate that corresponds to the question, its uncertainty, and enough information about the sample and design for readers to interpret it. Include an appropriate effect-size measure where relevant. The ICPSR test-selection guide connects test choices with hypotheses, test statistics, and effect-size measures.
Best Value
- State the outcome, predictors or groups, and comparison or relationship being estimated.
- Identify the method and any important design feature it accounts for, such as pairing or covariate adjustment.
- Report the estimate and uncertainty, not only whether a result crossed a significance threshold.
- Describe the effect size in terms readers can interpret for the subject area.
- Keep association, prediction, and causation distinct; an association or predictive model alone does not prove a causal effect.
What if more than one test seems plausible?
Compare candidate methods using the question they answer, the scales of the outcome and predictors, the dependence structure, assumptions and sensitivity to violations, and the effect-size and uncertainty outputs you need. If two methods differ in their target—for example, a mean comparison versus a rank-based distributional comparison—choose based on the scientific question, not which result is more favorable. A basic comparison table cannot cover every specialized design. Seek a statistician or a discipline-specific methods guide when the study involves complex dependence, sparse data, substantial confounding, or a target the common procedures do not directly address.
Can software choose the right test for me?
Software can run an analysis, but it cannot make the research question and design compatible by itself. The jamovi project describes its software as “a free and open statistical spreadsheet, designed to be easy to use and powered by the R statistical language.” Its site offers desktop software and a cloud option; features and service details can change. Whatever package you use, decide what to estimate and verify that the procedure fits before interpreting its output.
For further learning, JASP’s resources and materials include *Discovering Statistics Using JASP* and a free 2025 beginner tutorial text. SAGE also presents Andy Field’s second edition of Discovering Statistics Using R and RStudio as a hands-on book. These are optional learning resources, not prerequisites for choosing a defensible analysis.
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