Explain the difference between Type I and Type II errors in hypothesis testing.

Explain the difference between Type I and Type II errors in hypothesis testing.

• A. Type I error is incorrectly rejecting a true null hypothesis (false positive); Type II error is failing to reject a false null hypothesis (false negative).
• B. Type I error is failing to reject a false null; Type II error is correctly rejecting null.
• C. Type I and Type II are about sampling bias vs measurement error.
• D. Type I is a correct rejection; Type II is a correct non-rejection.

Answer: (D)

Understanding hypothesis testing errors means looking at what happens when you decide to reject or not reject the null hypothesis. A Type I error occurs when you reject a true null hypothesis—that’s a false positive. You conclude there is an effect when there isn’t one. The chance of making this error is the significance level, often denoted alpha. A Type II error happens when you fail to reject a false null hypothesis—that’s a false negative. You miss an effect that really exists. The probability of this error is beta, and the study’s power is 1 minus beta. So the correct description is that a Type I error is rejecting a true null (false positive) and a Type II error is failing to reject a false null (false negative). Statements about correct rejections or correct non-rejections describe accurate decisions, not errors, so they don’t capture the intended concept.

Understanding hypothesis testing errors means looking at what happens when you decide to reject or not reject the null hypothesis. A Type I error occurs when you reject a true null hypothesis—that’s a false positive. You conclude there is an effect when there isn’t one. The chance of making this error is the significance level, often denoted alpha.

A Type II error happens when you fail to reject a false null hypothesis—that’s a false negative. You miss an effect that really exists. The probability of this error is beta, and the study’s power is 1 minus beta.

So the correct description is that a Type I error is rejecting a true null (false positive) and a Type II error is failing to reject a false null (false negative). Statements about correct rejections or correct non-rejections describe accurate decisions, not errors, so they don’t capture the intended concept.