t-tests are great at one thing: two-group comparisons. The moment you care about three or more conditions, t-tests turn into a whack-a-mole of pairwise guesses, each swing adding error. ANOVA reframes the puzzle. Instead of asking six small questions, it asks one: do these group means differ more than we’d expect from within-group noise? That single test protects your error rate and clarifies logic: total variation = between-groups signal + within-groups noise. If the between-groups piece is meaningfully larger, you’ve earned the right to look closer (planned contrasts or post-hoc tests, reported with effect sizes and intervals). The principle scales: clear model first, surgical follow-ups second. Don’t stitch together conclusions from scattered p-values. Build one coherent question, one gatekeeping test, and only then ask where the differences live. Structure beats improvisation… especially when decisions ride on your answer.
