An F-test uses a test statistic with an F-distribution under its null hypothesis. For independent normal statistical samples of sizes , equality of population variances gives . For nested linear models with normal errors, the partial F-test compares the reduction in residual sum of squares to the remaining residual variation per degree of freedom. These are different hypotheses sharing a distributional construction. Exact calibration depends on the relevant independence and normality assumptions.[ref]
New to topics? Read the docs here!