Which of the following errors occurs when a true null hypothesis is not rejected?

A Type II error occurs when a true null hypothesis is not rejected. In the context of hypothesis testing, the null hypothesis generally posits that there is no effect or no difference in the population. When researchers fail to reject a true null hypothesis, this means they incorrectly conclude that there is not enough evidence to suggest an effect or difference when, in fact, the null hypothesis is true. This could happen due to insufficient sample size, low statistical power, or variability in the data.

Understanding Type II errors is crucial in program evaluation and research, as they can lead to missed opportunities for identifying real effects or differences in intervention outcomes. Recognizing this type of error helps researchers ensure that their studies are adequately powered and designed to detect significant effects when they truly exist. This contrasts with the other options, which do not describe the failure to reject a true null hypothesis.