Mann-Whitney U Test Calculator

Paste two independent samples to calculate U1, U2, the smaller U, tie-corrected z, approximate two-sided p-value, and effect-size context without assuming normality.

Group A

Group B

Apply 0.5 continuity correction to the normal approximation

—Smaller U statistic

—Approx. p-value

—Tie-corrected z

—|r| effect

—Total n

Statistic	Value	Context

This implementation uses average ranks for ties and a tie-corrected normal approximation. It does not claim an exact permutation p-value for small samples.

Mann-Whitney U: Ranks, Effect Size & Tie Audit

Audit Mann-Whitney U with sample medians, ties, rank-biserial effect size, common-language probability and exact-test eligibility.

Significance α

Core describe → test → interpret workflows

Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.

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Interpret statistical results in context

Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.

Practical guide and verification

Use the tool first, then use these checks to interpret, verify and hand off the result without displacing the primary workflow.

Confirm the samples are independent

The Mann–Whitney U test is designed for two independent groups. Repeated measurements on the same subjects or matched pairs require a paired method instead. Label the groups and verify the study design before pasting values; a correct U statistic cannot repair a mismatch between the test and the sampling structure.

Interpret the test as a rank-distribution comparison

The test is often described as comparing medians, but that interpretation needs similarly shaped distributions. In general it assesses rank ordering between groups. Review medians, ranks, effect-size measures and the actual distributions together rather than translating a small p-value automatically into a median-only claim.

Review ties and small samples

Tied observations affect the variance used by the normal approximation, and very small untied samples may be suitable for exact methods. Record whether the displayed p-value is exact or approximate and whether tie correction was used. Borderline results deserve extra care when sample size is small or ties are numerous.

Report effect size with statistical evidence

A p-value does not show the magnitude of separation. Pair U and p with rank-biserial or common-language effect evidence already exposed by the tool, plus sample sizes and descriptive summaries. Statistical significance should not be presented as practical importance without context.