Which statement correctly describes Type I and Type II errors?

• A. Type I error occurs when the null hypothesis is true; Type II error occurs when the null hypothesis is false.
• B. Type I error occurs when you fail to reject a true null; Type II error occurs when you reject a false null.
• C. Type I error occurs when you reject a false null; Type II error occurs when you fail to reject a true null.
• D. Type I error occurs when you fail to reject a true null; Type II error occurs when you reject a false null.

Answer: (A)

In hypothesis testing, Type I and Type II errors describe opposite mistakes researchers can make. A Type I error happens when you reject a null hypothesis that is actually true—this is a false positive. A Type II error happens when you fail to reject a null hypothesis that is actually false—this is a false negative. So the correct wording is: Type I error = reject a true null; Type II error = fail to reject a false null. The given description that Type I occurs when the null is true and Type II occurs when the null is false does not align with these definitions. In practice, alpha (the significance level) controls the probability of a Type I error, while beta relates to the probability of a Type II error; power, which is 1 minus beta, reflects the test’s ability to detect real effects. For example, if there is no real effect but you declare there is one, that’s a Type I error; if there is a real effect but you conclude there isn’t, that’s a Type II error.