BCPS Non-clinical Practice Test

What is a common error when comparing means across more than two groups?

Using ANOVA Using multiple t-tests Using chi-square tests Using Fisher exact test

Correct Answer:

Using multiple t-tests

Explanation:

When comparing means across more than two groups, doing separate t-tests for every pair dramatically increases the chance of a false positive due to multiple comparisons. The standard approach is to use analysis of variance (ANOVA), which assesses whether any group mean differs from the others while keeping the overall Type I error rate at the chosen level. If ANOVA shows a significant effect, follow with post hoc tests (such as Tukey or Bonferroni) to determine which specific groups differ, with adjustments to control for multiple testing. Chi-square and Fisher exact tests are for categorical outcomes, not means, so they aren’t appropriate here.

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