What does statistical power measure?

Statistical power is fundamentally concerned with the likelihood of correctly rejecting the null hypothesis when it is indeed false. In other words, it quantifies the ability of a statistical test to detect an effect when there is one. A higher power indicates a greater chance of identifying a true effect, which is crucial for making accurate inferences in research.

When we discuss statistical power, we often refer to several factors that influence it, including sample size, effect size, and significance level. While these factors are important in the context of power calculations, the primary definition centers on the probability aspect. This is why the focus is on the ability to reject the null hypothesis correctly, which is a central concern in hypothesis testing.

The other options relate to important aspects of research design, but they do not capture the essence of what statistical power measures. The sample size needed for a study does relate to power because larger sample sizes generally increase power, but that is not the definition of power itself. Similarly, effect size refers to the magnitude of the phenomenon being studied, while the accuracy of survey data collection concerns data reliability and validity, neither of which directly defines power. Therefore, the correct answer correctly identifies the fundamental concept of statistical power in hypothesis testing.