Power is?

Power is?

• A. The probability of making a Type I error.
• B. The probability of failing to reject a false null.
• C. The probability the test will correctly reject a false Null hypothesis.
• D. The probability of observing a large effect.

Answer: (D)

Power is the probability that a statistical test will detect a real effect when one exists. In practical terms, it’s the chance the test will reject the null hypothesis when the null is false. This equals 1 minus the probability of a Type II error (failing to reject a false null). Power increases with larger true effect sizes, larger sample sizes, and lower variability in measurements; it can also be influenced by the chosen significance level (a higher alpha can raise power but increases the risk of a Type I error). It is not about the observed size of the effect itself—observing a large effect is not the same as having power.

Power is the probability that a statistical test will detect a real effect when one exists. In practical terms, it’s the chance the test will reject the null hypothesis when the null is false. This equals 1 minus the probability of a Type II error (failing to reject a false null). Power increases with larger true effect sizes, larger sample sizes, and lower variability in measurements; it can also be influenced by the chosen significance level (a higher alpha can raise power but increases the risk of a Type I error). It is not about the observed size of the effect itself—observing a large effect is not the same as having power.