Interpretation of eta-squared value in ANOVA?

• A. The mean difference between groups.
• B. The error term in the model.
• C. The Proportion of total variance in the dependent variable explained by the independent variable.
• D. The p-value associated with the F statistic.

Answer: (C)

Eta-squared shows how much of the total variation in the dependent variable is explained by the independent variable. It’s computed as the sum of squares for the effect divided by the total sum of squares, so it directly answers how much of the overall variability is due to group differences. For example, if the effect accounts for 25 of 100 total sum-of-squares, eta-squared is 0.25, meaning 25% of the variance is explained by the factor. This is an effect size, not a test of significance—the p-value from the F statistic tells you whether the observed effect is unlikely under the null, but not how much variance is explained. The mean difference between groups describes average differences, not the proportion of total variance; the error term reflects the unexplained within-group variability; and the p-value relates to significance, not the amount of variance explained. In simple one-way ANOVA, eta-squared is a straightforward measure of how large the effect is in terms of variance explained.