Critical Inquiry Exam 1 Practice

The main idea is how confident we are in detecting a real effect. Power is the probability of correctly rejecting a false null hypothesis, while Type II error is the probability of failing to reject a false null. They are connected by power = 1 minus beta, where beta is the Type II error rate. So when you increase power, beta decreases, meaning the chance of missing a true effect goes down. Methods to raise power include larger sample size, a larger true effect, or a design that improves sensitivity, all of which help detect real differences without necessarily inflating false positives. If you adjust alpha upward, you might increase Type I error, but the direct relationship between power and Type II error is inverse: higher power means lower Type II error. Therefore increasing power reduces the probability of a Type II error.