A statistical computation performed after a study has concluded, using the observed effect size, sample size, and alpha level to estimate the probability of detecting a true effect. For example, if a study fails to reject the null hypothesis, this calculation aims to determine if the failure was due to a lack of statistical power rather than a genuine absence of effect.
Understanding the achieved power provides context to non-significant findings. Historically, it has been used to justify underpowered studies or to claim that a non-significant result is “almost significant.” However, this application is often criticized because the computed value is directly related to the p-value and offers no additional information beyond what the p-value already conveys. Its use may lead to misinterpretations about the reliability and validity of research findings.
