Which factors determine statistical power?

Which factors determine statistical power?

• A. Effect size and p-value
• B. Sample size and alpha
• C. Effect size, alpha, sample size, and variability
• D. Confidence interval width

Answer: (C)

Statistical power is the probability that a study will detect a true effect if there is one. It hinges on four factors: the size of the true effect (larger effects are easier to detect, increasing power), the significance threshold you set (a higher alpha makes it easier to reject the null, increasing power but also increasing the chance of a false positive), the amount of data (larger sample sizes give more precise estimates and raise power), and the variability in the data (more variability makes it harder to detect an effect, lowering power). P-values are the outcome of the test, not a determinant of power, so they aren’t factors that power depends on. Confidence interval width reflects precision and is influenced by sample size and variability, but it isn’t itself a direct determinant of power.

Statistical power is the probability that a study will detect a true effect if there is one. It hinges on four factors: the size of the true effect (larger effects are easier to detect, increasing power), the significance threshold you set (a higher alpha makes it easier to reject the null, increasing power but also increasing the chance of a false positive), the amount of data (larger sample sizes give more precise estimates and raise power), and the variability in the data (more variability makes it harder to detect an effect, lowering power). P-values are the outcome of the test, not a determinant of power, so they aren’t factors that power depends on. Confidence interval width reflects precision and is influenced by sample size and variability, but it isn’t itself a direct determinant of power.