Because of the relationship between ANOVA and multiple regression, we can use the correlation coefficient as a measure of effect size in ANOVA. The value of the correlation coefficient is given by Multiple R in the Regression data analysis tool. E.g., for Example 1 of ANOVA using Regression, *r* = .285 (see Figure 2 of ANOVA using Regression), which indicates a medium effect (since *r* ≈ .3).

The correlation coefficient metric (often called **eta**) is based on the data from the sample, but doesn’t always provide a good representation for the population as a whole. As a result, another measure of effect size, called **omega**, is more commonly used. Omega is given by the following formula:

The first version uses the terminology of regression analysis, while second uses the terminology of ANOVA. We also have the following alternative form:

For the one factor ANOVA in Example 3 of Basic Concepts for ANOVA, *ω ^{2}* = 0.14 (as can be seen in Figure 1 of Confidence Interval for ANOVA). For the two factor ANOVA in Example 2 of ANOVA using Regression we calculate omega square as follows:

In general, omega is a more accurate measure of the effect, where *ω ^{2}* = .01 is considered a small effect and

*ω*= .06 and .14 are considered medium and large effects respectively.

^{2}Effect sizes for the omnibus ANOVA results, however, are not really that interesting. More useful are effect sizes for planned tests. As explained in Linear Regression for Comparing Means, a useful measure of effect size here is