About the Mann Whitney U Test
The Mann Whitney U test compares two independent groups. It is useful when data are ordinal, skewed, or not safely treated as normal. The method ranks all values together. Then it checks whether ranks from one group tend to be higher than ranks from the other group.
Why This Calculator Helps
This calculator follows a GraphPad style workflow. You enter two samples, choose a tail, and decide whether to use exact or normal approximation. The tool handles ties with average ranks. It also reports U values, rank sums, z score, p value, effect size, and a Hodges Lehmann median difference. These outputs help you review both significance and practical size.
Interpreting Results
A small p value suggests the groups differ in distribution. It does not prove that medians differ in every setting. The test mainly evaluates whether observations from one group are generally larger than observations from the other group. The rank biserial correlation shows direction and strength. Positive values mean Group A tends to be larger. Negative values mean Group B tends to be larger.
Data Quality Notes
Each group should contain independent observations. Do not enter paired before and after data. Use a paired rank test for that design. Outliers are allowed, but they still affect ranks. Ties are accepted, although many ties make exact methods less suitable. The normal approximation becomes more stable as sample sizes increase.
Reporting the Test
A clear report should include group sizes, U statistic, p value, tail choice, and effect size. Also mention whether exact or normal approximation was used. If your work must match a journal or classroom requirement, check that the same tail and continuity correction are selected. Different settings can change the p value slightly.
When to Choose It
Use this test when two unrelated samples are measured on the same scale. It is common for clinical scores, survey ratings, laboratory values, and small experimental studies. The calculator is not a replacement for study design. It gives a fast statistical check, but your conclusion should also consider sampling, measurement quality, and the real question being asked. Save exports when you need transparent records for later review. Keep raw data with every reported result.