In statistics, power refers to the ability of a statistical test to detect a true effect or relationship when it exists. A more powerful test has a higher probability of correctly rejecting the null hypothesis.
Related terms
Sample Size: The number of observations or individuals included in a study. Increasing sample size generally increases the power of a statistical test.
Effect Size: The magnitude of an observed difference or relationship between variables. Larger effect sizes increase the power of a statistical test.
Alpha Level: Also known as the significance level, it represents the threshold below which we reject the null hypothesis. A lower alpha level increases the power of a statistical test but may also increase the risk of Type I errors.