What is statistical power in research?

Statistical power refers specifically to the probability of correctly rejecting a false null hypothesis. This concept is pivotal in hypothesis testing and is indicative of a study’s ability to detect an effect if one truly exists. High statistical power means that there is a strong likelihood that the study will identify a statistically significant effect when there is one, thereby reducing the risk of a Type II error, which occurs when a false null hypothesis is retained.

In practical terms, power is influenced by several factors including sample size, effect size, and significance level. A larger sample size typically increases the power of a study because it can provide more reliable estimates of the population parameters. This makes the study more capable of identifying true relationships or differences within the data.

Understanding statistical power is crucial for designing robust research and interpreting study results accurately. It allows researchers to assess whether their findings can be confidently attributed to the effects being studied rather than occurring by chance.