Compare means across 3+ groups with post-hoc Tukey tests. Free.
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Running one-way anova group comparison analysis...
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Analyze another fileTests whether the average of a numeric measurement differs across 3 or more groups. If it does, post-hoc Tukey tests show which specific pairs of groups differ.
Use this when comparing means across 3+ groups (e.g., treatment arms, product variants, regions).
If you only have 2 groups, use T-Test. If data is non-normal, use Kruskal-Wallis.
Built for: Researcher, analyst, product manager, quality engineer
Typical data source: Numeric measurements with a categorical group label (3+ groups)
Group comparison data
Minimum 15 rows · Best with 50-1000 observations
Performs a one-way Analysis of Variance (ANOVA) to test whether the means of a numeric measurement differ significantly across three or more groups. Includes F-statistic, p-value, eta-squared effect size, Tukey post-hoc comparisons, and assumption diagnostics.
Mean measurement per group with confidence intervals
Distribution of measurements within each group
F-test for overall group differences
Pairwise group comparisons with multiplicity correction
Mean differences with confidence intervals — significant pairs highlighted
Normality (Shapiro-Wilk) and homogeneity of variance (Levene's test)
Descriptive statistics by group
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? T Test — Only 2 groups
Need more power? Ancova — Need to control for covariates
Similar: Kruskal Wallis
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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