In hypothesis testing, what does "statistical power" indicate?

Statistical power refers to the probability of correctly rejecting the null hypothesis when it is false, which means that the research design is adequate to detect an effect if there is one. A high statistical power indicates that the study is more likely to identify a significant effect, should it exist, reducing the likelihood of a Type II error (failing to reject the null hypothesis when it is false).

In hypothesis testing, power is influenced by several factors, including sample size, effect size, and the chosen significance level. Higher power can be achieved through larger sample sizes or by having a larger actual effect in the population. The ability to accurately reject the null hypothesis when it is false is crucial for the validity of the results and the overall conclusions drawn from a study.