What determines the statistical power of a study?

What determines the statistical power of a study?

• A. Alpha level alone
• B. Sample size alone
• C. P-value
• D. Effect size, alpha, sample size, and variability

Answer: (D)

The power of a study is the probability of correctly detecting a real effect when one exists. It depends on four factors: the true effect size, the significance threshold (alpha), the number of observations (sample size), and how much variability or noise is in the data. A larger true effect is easier to detect, so power increases with effect size. More observations reduce the uncertainty around the estimated effect, so power goes up as sample size grows. A higher alpha makes it easier to declare a result significant, which increases power but also raises the chance of a false positive. Lower variability means the data are more precise, making it easier to distinguish the real effect from random fluctuation, which also raises power. P-value is not a determinant of power itself; it’s a result that reflects all those factors after you collect the data. So you can’t determine power from the p-value alone. The four factors together define power, and power analysis uses them to plan studies or interpret their findings.

The power of a study is the probability of correctly detecting a real effect when one exists. It depends on four factors: the true effect size, the significance threshold (alpha), the number of observations (sample size), and how much variability or noise is in the data.

A larger true effect is easier to detect, so power increases with effect size. More observations reduce the uncertainty around the estimated effect, so power goes up as sample size grows. A higher alpha makes it easier to declare a result significant, which increases power but also raises the chance of a false positive. Lower variability means the data are more precise, making it easier to distinguish the real effect from random fluctuation, which also raises power.

P-value is not a determinant of power itself; it’s a result that reflects all those factors after you collect the data. So you can’t determine power from the p-value alone. The four factors together define power, and power analysis uses them to plan studies or interpret their findings.