Type II 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 false null hypothesis
• D. Failing to reject a true null hypothesis

Answer: (C)

Type II error happens when the null hypothesis is false but you fail to reject it. In other words, there’s a real effect or difference, but the study doesn’t detect it, a false negative. The opposite mistake is rejecting a true null hypothesis, which is a Type I error. Failing to reject a true null hypothesis is a correct decision—no effect detected when none exists. Failing to reject a false null hypothesis is exactly the situation described by Type II error.

The chance of making this error is beta, and the test’s power is 1 minus beta, the probability of correctly detecting a real effect. To reduce Type II error, you can increase sample size, observe a larger effect, adjust the study design to be more sensitive, or decrease variability in measurements.