What is power in a study?

• A. Probability of a Type I error
• B. Probability that a study will find no significant difference when a difference exists
• C. Probability that a study will find a statistically significant difference where one exists
• D. The probability that the null hypothesis is true

Answer: (C)

Power is the probability that a study will detect a true difference if one exists. In other words, it’s the chance of obtaining a statistically significant result when the alternative hypothesis is true. This is 1 minus beta, where beta is the probability of a Type II error—failing to reject the null even though a real effect is present.

Power increases when the true difference is larger, the variability around measurements is smaller, and the sample size is bigger. It also goes up if you set a higher significance level (alpha), though that comes with a greater risk of a false positive. Power is not the probability the null is true, nor the probability of finding no difference when there is one.