Direct answer
Choose a statistical test only after defining the target effect, outcome type, study design, number of groups, independence or pairing, model assumptions, and whether estimation or testing is the primary goal.
Visual explanation
Design first, method second
What this calculation tells you
A statistical test is one component of an analysis plan. Different tests answer different null questions even when they accept similarly shaped input tables.
Start with the estimand: mean difference, proportion difference, association, distributional shift, variance, rate, or another target. Then map the actual sampling unit and dependence structure.
Where it is used
Research planning
Translate a question and design into an auditable candidate analysis.
Quality
Separate paired process changes from independent group comparisons.
Education
Organize method selection around estimands and sampling units.
Review
Identify mismatches between study design and reported test.
Common situations
- Comparing two independent groups.
- Analyzing before-and-after measurements.
- Testing association between categories.
- Comparing several groups or repeated conditions.
Start with the statistical question
Start with the estimand: mean difference, proportion difference, association, distributional shift, variance, rate, or another target. Then map the actual sampling unit and dependence structure.
A branching decision map moves from numerical or categorical outcome through group count and pairing to candidate families, with assumption checkpoints and an estimation-first exit.
Worked example
Two measurements from the same participants form paired differences, so an independent two-sample test discards the pairing. Two separate groups do not become paired merely because their sizes match.
Assumptions that carry the result
Every candidate has design and distributional conditions. Nonparametric does not mean assumption-free, and a normality pretest is not a universal automatic switch.
Interpret the result without overreaching
The selector cannot judge data collection, causal identification, multiplicity, missingness, measurement validity, protocol deviations, or whether the target question matters.
- Choosing from variable names rather than design.
- Treating repeated measurements as independent rows.
- Running several tests and reporting only the smallest p-value.
Practical questions
Frequently asked questions
Can a calculator choose the test automatically?
It can organize candidates from entered design features, but it cannot verify the scientific question, collection process, assumptions, or consequences.
Are nonparametric tests assumption-free?
No. They still require appropriate sampling, independence or pairing, outcome ordering, and an interpretation matched to their statistic.
Should I test normality first?
A mechanical pretest-and-switch workflow can distort error properties; inspect design, residuals, robustness, and model purpose together.
Further reading
Authoritative sources
Use these primary and professional resources to check definitions, conventions, or requirements that may extend beyond this guide.