A researcher performs multiple subgroup analyses in a single RCT without pre-specifying them in the protocol. The probability of a Type I error across all comparisons is BEST reduced by:

• A Using one-tailed instead of two-tailed tests
• B Increasing the sample size
• C Bonferroni correction ✓
• D Switching from parametric to non-parametric tests

Explanation

When multiple comparisons are made, the probability of a spurious significant result (Type I error) increases dramatically — with 20 tests at alpha = 0.05, one false positive is expected by chance alone. Bonferroni correction adjusts the significance threshold by dividing alpha by the number of comparisons (e.g., 0.05/10 = 0.005 for 10 tests), controlling the family-wise error rate. One-tailed tests actually increase Type I error risk; sample size affects power (Type II error), not multiple comparisons correction.

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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