Which statement best describes statistical power?

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Multiple Choice

Which statement best describes statistical power?

Statistical power is the likelihood that a study will detect a true effect. It represents the probability of correctly rejecting the null hypothesis when a real effect exists. Put simply, power is 1 minus the probability of a Type II error (failing to detect an effect that is there). Power increases when the true effect is larger, the sample size is bigger, and there is less variability in the data; it also changes with the chosen significance level—raising alpha can increase power but also raises the chance of a false positive. This description directly captures what power measures: our study’s ability to identify real effects. The other statements describe related but different concepts. A describes the chance of finding significance when there is no real effect (the false positive rate, or alpha). C speaks to the probability about the truth of the null itself, not the study’s ability to detect effects. D concerns bias and data quality, not the test’s capacity to detect true effects.

Statistical power is the likelihood that a study will detect a true effect. It represents the probability of correctly rejecting the null hypothesis when a real effect exists. Put simply, power is 1 minus the probability of a Type II error (failing to detect an effect that is there). Power increases when the true effect is larger, the sample size is bigger, and there is less variability in the data; it also changes with the chosen significance level—raising alpha can increase power but also raises the chance of a false positive.

This description directly captures what power measures: our study’s ability to identify real effects. The other statements describe related but different concepts. A describes the chance of finding significance when there is no real effect (the false positive rate, or alpha). C speaks to the probability about the truth of the null itself, not the study’s ability to detect effects. D concerns bias and data quality, not the test’s capacity to detect true effects.

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