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Compare the means of two to six groups with a one-way ANOVA. Enter raw data or each group's n, mean and SD to get the full ANOVA table, F, p and effect sizes.
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| Source | SS | df | MS | F | p |
|---|---|---|---|---|---|
| Between groups | 65.106 | 2 | 32.553 | 20.608 | < 0.001 |
| Within groups | 33.173 | 21 | 1.580 | ||
| Total | 98.278 | 23 |
| Eta squared (η²) | 0.662 |
|---|---|
| Omega squared (ω²) | 0.620Less biased for small samples |
A significant F tells you the groups differ somewhere; follow up with post-hoc tests (e.g. Tukey) to see which.
Raw values or summary statistics for each group.
Add up to six groups.
Sums of squares, F, p, η² and ω².
One-way analysis of variance tests whether the means of three or more independent groups are equal, by comparing the variation between groups with the variation within groups.
A significant F-test says the groups are not all equal, but not which ones differ; post-hoc tests such as Tukey's HSD answer that. If the data are clearly non-normal, the Kruskal-Wallis test is the rank-based alternative.
Yes. A one-way ANOVA can be calculated exactly from each group's n, mean and SD.
Both describe the share of variance explained by group membership; omega squared corrects the upward bias of eta squared in small samples.