What is the distinction between Type I and Type II errors in hypothesis testing?

• A. Type I error = false positive (rejecting a true null); Type II error = false negative (failing to reject a false null).
• B. Type I error = false negative; Type II error = false positive.
• C. Type I error = correct rejection; Type II error = failed to reject.
• D. Type I error = sampling bias; Type II error = confounding.

Answer: (A)

When you run a hypothesis test, you decide whether to reject the null hypothesis. A Type I error occurs if you reject the null even though it is true — a false positive: you claim there is an effect when there isn’t. A Type II error happens if you fail to reject the null when it is false — a false negative: you miss a real effect. These errors are tied to the significance level and the test’s power: lowering the alpha level reduces Type I errors but can increase Type II errors, while increasing power reduces Type II errors. In simple terms, you’re risking saying an effect exists when it doesn’t, or missing an effect that does exist.