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Partial eta-squared is a measure of variance, like r-squared. It tells us what proportion of the variance in the dependent variable is attributable to the factor in question. You can get these measures by choosing the 'Estimates of effect size' option when setting up an ANOVA.
Consider first the case where the target is a constantâsay, the parameter âand denote the mean of the estimator as . The mean squared error can then be decomposed as. The mean squared error thus comprises the variance of the estimator and the squared bias. The two components can be associated with an estimatorâs precision (small variance) and its accuracy (small bias). If is an unbiased estimator of âthat is, if âthen the mean squared error is simply the variance of the estimator. By choosing an estimator that has minimum variance, you also choose an estimator that has minimum mean squared error among all unbiased estimators.