Compare distributions across 3+ groups without normality assumption. Free.
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Running kruskal-wallis non-parametric group comparison analysis...
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Analyze another fileNon-parametric alternative to ANOVA — tests whether distributions differ across 3+ groups without assuming normality. Uses ranks instead of raw values, making it robust to outliers and skewed data.
Use this when you have 3+ groups and the data is ordinal, skewed, or violates ANOVA's normality assumption.
If your data is roughly normal, use ANOVA (more powerful). If you have only 2 groups, use Mann-Whitney.
Built for: Researcher, biostatistician, quality analyst, social scientist
Typical data source: Numeric measurements across 3+ groups where normality may not hold
Measurement data across groups
Minimum 15 rows · Best with 50-1000 observations
Non-parametric alternative to one-way ANOVA -- tests whether rank distributions differ across 3 or more groups without requiring normality assumptions. Includes Dunn post-hoc pairwise tests with Bonferroni correction. Ideal for ordinal data, small samples, or heavily skewed distributions.
H statistic, p-value, and epsilon-squared effect size
Median values with interquartile range per group
Outcome distributions per group
Pairwise group comparisons with Bonferroni correction
Rank positions per group
Sample size, median, and mean rank per group
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Mann Whitney — Only 2 groups
Need more power? Anova — Data meets normality assumptions
Similar: Anova
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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