Bonferroni correction is used to control which type of error?

• A. Type I error
• B. Type II error
• C. Sampling bias
• D. Measurement error

Answer: (A)

When you run many statistical tests, the chance of getting at least one false positive grows. Bonferroni correction targets this issue by controlling the overall risk of making a Type I error across all tests. It does this by tightening the per-test significance level: divide the overall alpha by the number of tests, and use that smaller threshold for each individual test. For example, with five tests and an overall alpha of 0.05, each test would be evaluated at 0.01.

A Type I error is when you incorrectly conclude there is an effect when there isn’t one (a false positive). Bonferroni is specifically designed to reduce this kind of error when multiple comparisons are made.

Keep in mind that making the test criteria stricter can reduce the ability to detect real effects, potentially increasing Type II errors (false negatives). Bonferroni doesn’t address sampling bias or measurement error, which are separate data-quality issues.