What is a Type I error in hypothesis testing?

• A. False positive
• B. False negative
• C. True positive
• D. No error

Answer: (A)

A Type I error occurs when you reject the null hypothesis even though it is true. In plain terms, you claim there is an effect or difference when none actually exists—this is a false positive. The decision rule in hypothesis testing is set by the significance level (alpha); if the data produce a result at or below alpha, you reject the null, and if the null is true, that decision is a Type I error with probability alpha. This is distinct from a Type II error, which happens when you fail to reject a null hypothesis that is actually false (missing a real effect). A correct decision to reject a false null would be a valid detection of an effect, not typically labeled as a “true positive” in this framework.