Quantitative Business Analysis (QBA) Exam 2 Practice Test

Power is the probability that a statistical test will correctly reject a false null hypothesis, i.e., it detects an effect when one truly exists. This is why the correct description is the probability of rejecting a false null. Power is equal to 1 minus the probability of a Type II error (failing to reject a false null). The other concepts refer to different error rates: rejecting a true null is a Type I error; not rejecting a true null is the complement of the significance level; and failing to reject a false null is the Type II error. Power increases with larger sample size, larger true effect size, less variability, and, depending on the context, may be affected by the chosen significance level (higher alpha can raise power but also increases the risk of a Type I error).