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MANN-WHITNEY U TEST CALCULATOR

Mann-Whitney U test calculator with exact p values and effect size.

Compare two independent groups when the data are ordinal (such as Likert items) or not normally distributed. You get U, z, the p value (exact for small samples without ties), medians, mean ranks, effect size r, and an APA sentence.

DATA

Two independent groups

NON-PARAMETRIC

Separate values with commas, spaces, or new lines. Paste a column from Excel.

METHOD

Mann-Whitney U test: when to use it, formula, and reporting

What is the Mann-Whitney U test?

It compares two independent groups by ranking all values together and checking whether one group tends to have higher ranks. It is the non-parametric alternative to the independent-samples t-test, also called the Wilcoxon rank-sum test.

Formula

U₁ = R₁ − n₁(n₁ + 1) ÷ 2, U₂ = n₁n₂ − U₁, U = min(U₁, U₂)

R₁ is the sum of ranks in group 1. Tied values share the average rank. For larger samples, z = (U₁ − n₁n₂/2) ÷ σ_U, with a tie correction in σ_U.

Mann-Whitney U vs t-test

  • Use the t-test when the outcome is numeric and roughly normal in each group
  • Use Mann-Whitney for single Likert items, ranks, or skewed data with outliers
  • Mann-Whitney compares distributions (often summarised as medians); the t-test compares means
  • With very small groups (under about 10 each), Mann-Whitney is usually the safer choice

Mann-Whitney U vs Wilcoxon signed-rank

Mann-Whitney is for two different groups of people. Wilcoxon signed-rank is for the same people measured twice, such as a pre-test and post-test.

How to report it (APA style)

“A Mann-Whitney U test indicated that satisfaction was significantly higher for in-person students (Mdn = 7.5) than for online students (Mdn = 3.5), U = 2, z = −2.58, p = .013, r = .74.” Report medians, U, z, p, and an effect size.

References

  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.
  • Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18.