Which statistical error is defined as rejecting the null hypothesis when it is true?

Which statistical error is defined as rejecting the null hypothesis when it is true?

• A. Type II error
• B. Type I error
• C. Regression toward the mean
• D. Power

Answer: (B)

Rejecting the null hypothesis when it is actually true is a Type I error, also known as a false positive. This error arises because the significance level, alpha, sets the maximum probability you’re willing to reject the null when it’s true. If the null is true, there’s still a chance—equal to alpha—that the data appear significant and lead you to reject it. For example, with alpha set at 0.05, about 5% of studies where the null is true will spuriously show a significant result. A Type II error, by contrast, is failing to reject a false null. Regression toward the mean is a separate phenomenon where extreme measurements tend to move closer to the average on repetition, not a decision error in hypothesis testing. Power is the probability of correctly rejecting a false null (1 minus beta).

Rejecting the null hypothesis when it is actually true is a Type I error, also known as a false positive. This error arises because the significance level, alpha, sets the maximum probability you’re willing to reject the null when it’s true. If the null is true, there’s still a chance—equal to alpha—that the data appear significant and lead you to reject it. For example, with alpha set at 0.05, about 5% of studies where the null is true will spuriously show a significant result. A Type II error, by contrast, is failing to reject a false null. Regression toward the mean is a separate phenomenon where extreme measurements tend to move closer to the average on repetition, not a decision error in hypothesis testing. Power is the probability of correctly rejecting a false null (1 minus beta).