How is statistical power defined?

How is statistical power defined?

• A. The probability of rejecting a true null hypothesis.
• B. The probability of failing to reject a false null hypothesis.
• C. The probability of correctly rejecting a false null hypothesis.
• D. The probability of obtaining a p-value less than 0.05.

Answer: (C)

Statistical power is the probability that your test will correctly reject a false null hypothesis when the alternative is true. In other words, it measures the test’s ability to detect an actual effect. Power equals 1 minus the probability of a Type II error (failing to reject a false null). It depends on the true effect size, sample size, data variability, and the chosen significance level. As the true effect grows or you collect more data, power increases; higher variability lowers power; increasing the significance level can raise power but also raises the chance of a false alarm. The option describing this ability precisely is the best match. Rejecting a true null is a Type I error, not power. Failing to reject a false null is a Type II error, not power. A p-value below a threshold relates to significance testing, not the power itself.

Statistical power is the probability that your test will correctly reject a false null hypothesis when the alternative is true. In other words, it measures the test’s ability to detect an actual effect. Power equals 1 minus the probability of a Type II error (failing to reject a false null). It depends on the true effect size, sample size, data variability, and the chosen significance level. As the true effect grows or you collect more data, power increases; higher variability lowers power; increasing the significance level can raise power but also raises the chance of a false alarm. The option describing this ability precisely is the best match. Rejecting a true null is a Type I error, not power. Failing to reject a false null is a Type II error, not power. A p-value below a threshold relates to significance testing, not the power itself.