What is a Type II error?

• 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 occurs when there is actually an effect (the null hypothesis is false), but the data don’t provide enough evidence to conclude that, so you fail to reject the null hypothesis. In other words, you miss a real signal and conclude there’s no effect when there really is one. The probability of making this error is called beta, and the power of a test is 1 minus beta, representing the chance of detecting an effect when it exists. Factors that influence this include sample size, the true size of the effect, the variability in the data, and the chosen significance level. For example, if a new drug truly improves outcomes but your study isn’t large enough or precise enough to show a statistically significant difference, that’s a Type II error. The other common error—rejecting a true null hypothesis—is a Type I error, while rejecting a false null hypothesis would be a correct detection, and failing to reject a true null is a correct non-detection.