In hypothesis testing, Type I error is defined as

In hypothesis testing, Type I error is defined as

• A. Rejecting a true null; Type II error is failing to reject a false null.
• B. Failing to reject a true null; Type II error is rejecting a false null.
• C. Rejecting a true null; Type II error is failing to reject a false null.
• D. Failing to reject a true null; Type II error is the p-value.

Answer: (C)

In hypothesis testing, two common mistakes can occur: Type I error and Type II error. A Type I error happens when we reject the null hypothesis even though it is true—it's like a false alarm. A Type II error happens when we fail to reject the null hypothesis when it is actually false—meaning we miss a real effect. So describing a Type I error as “rejecting a true null” and describing a Type II error as “failing to reject a false null” correctly captures both ideas. The p-value, on the other hand, is not an error type; it’s a measure used to decide whether to reject the null.

In hypothesis testing, two common mistakes can occur: Type I error and Type II error. A Type I error happens when we reject the null hypothesis even though it is true—it's like a false alarm. A Type II error happens when we fail to reject the null hypothesis when it is actually false—meaning we miss a real effect. So describing a Type I error as “rejecting a true null” and describing a Type II error as “failing to reject a false null” correctly captures both ideas. The p-value, on the other hand, is not an error type; it’s a measure used to decide whether to reject the null.