Which statement describes a major drawback of the Bonferroni correction?

• A. It increases the chance of false positives
• B. It makes it harder to find real effects, increasing false negatives
• C. It inflates the Type I error
• D. It biases mean estimates

Answer: (B)

The main idea here is how multiple comparisons affect our ability to detect real effects. The Bonferroni correction makes each individual test harder to declare significant by dividing the overall alpha level by the number of tests. This strongly reduces the chance of false positives across all tests, which is its purpose.

However, imposing a stricter significance threshold also reduces statistical power—the ability to detect true effects. When a real effect exists but the p-value doesn’t meet the tightened criterion, you end up not declaring it significant. This leads to more false negatives (missing real effects).

So the major drawback is the loss of power and the resulting increase in false negatives. The other options don’t fit: Bonferroni does not inflate false positives, it reduces them; it doesn’t bias mean estimates, and its main drawback isn’t about Type I error inflation.