An F-test of linear hypotheses is compared with Bonferroni t-tests. The individual confidence intervals from Bonferroni t-tests are uniformly shorter than S-intervals implied by the F-test. Power curves are constructed for a few specific alternative hypotheses as functions of the correlation between regressors for the special case of two hypotheses and two regressors. The power of the two procedures is similar when the correlation is small. For highly correlated regressors, however, the power of the Bonferroni method is generally inferior. Thus, if regressors can be controlled to be uncorrelated, the Bonferroni method is clearly superior; otherwise neither method dominates.