Type I error is defined as which of the following?

• A. Rejecting a true null hypothesis
• B. Rejecting a false null hypothesis
• C. Failing to reject a true null hypothesis
• D. Failing to reject a false null hypothesis

Answer: (A)

Understanding Type I error: it happens when you reject a true null hypothesis, a false positive. In hypothesis testing we pick a significance level (alpha) to limit how often this occurs. If there is actually no real effect, about alpha of studies will incorrectly conclude there is an effect. For example, finding that a drug works when it actually has no effect is a Type I error. The other situations describe either correctly rejecting a false null (a true signal detected), correctly failing to reject a true null, or failing to reject a false null (a Type II error).