Compare two groups without normality assumption. Upload CSV, get results. Free.
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Analyze another fileNon-parametric test comparing two independent groups using ranks. The go-to test when data is ordinal, skewed, or has outliers that make a t-test unreliable.
Use this when comparing two groups and data is non-normal, ordinal, or has outliers.
If data is roughly normal, use T-Test (more powerful). If you have 3+ groups, use Kruskal-Wallis.
Built for: Researcher, biostatistician, QA analyst
Typical data source: Measurements for two groups where normality may not hold
Two-group comparison data
Minimum 10 rows · Best with 30-1000 observations
Compares two independent groups using rank-based non-parametric test when normality assumptions are violated. Provides U statistic, p-value, rank-biserial effect size, and Hodges-Lehmann median difference estimate.
U statistic, p-value, and rank-biserial effect size
Descriptive statistics for each group (median, IQR, range)
Overlapping distributions showing data spread per group
Box plots comparing medians and spread between groups
How ranks are distributed across groups
Rank-biserial correlation and Hodges-Lehmann estimate with CI
Shapiro-Wilk tests justifying non-parametric approach
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? T Test — Data meets normality assumptions
Need more power? Kruskal Wallis — 3+ groups
Similar: T Test
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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