Define Power.

Define Power.

• A. The degree to which the sample represents the population
• B. The likelihood that the test will detect a difference between groups if there is one
• C. The probability of a type I error
• D. The size of the effect

Answer: (B)

Power is the probability that a statistical test will reject the null hypothesis when there truly is a difference between groups. In other words, it reflects the test’s ability to detect a real effect. This is 1 minus the probability of a Type II error (failing to detect a real difference). Power depends on sample size, the true size of the effect, data variability, and the chosen significance level. Larger samples, bigger true effects, and less variability increase power, while increasing the alpha level raises power but also raises the risk of a false positive. Researchers use power analysis to determine the sample size needed to reliably detect the expected difference.

Power is the probability that a statistical test will reject the null hypothesis when there truly is a difference between groups. In other words, it reflects the test’s ability to detect a real effect. This is 1 minus the probability of a Type II error (failing to detect a real difference). Power depends on sample size, the true size of the effect, data variability, and the chosen significance level. Larger samples, bigger true effects, and less variability increase power, while increasing the alpha level raises power but also raises the risk of a false positive. Researchers use power analysis to determine the sample size needed to reliably detect the expected difference.