Type II error is defined as which of the following?

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.

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.