Power in hypothesis testing is best described as what?

• A. Power is the probability of correctly rejecting a false null hypothesis.
• B. Power is the probability of making a Type I error.
• C. Power is the size of the sample.
• D. Power is the probability of obtaining a significant p-value.

Answer: (A)

Power measures how sensitive a test is to detect an effect that truly exists. It is the probability of rejecting the null hypothesis when the null is false, i.e., the chance of catching a real effect. This equals 1 minus the probability of a Type II error and tends to increase with larger true effects, bigger sample sizes, and less variability (and, to some extent, with a higher alpha, though that raises the risk of a Type I error). So the best description is the likelihood of correctly rejecting a false null hypothesis. The other statements describe the Type I error rate, the sample size, or the general chance of obtaining a significant p-value, which are not the standard definition of power.