What does a higher statistical power indicate in a study?

A higher statistical power in a study is indicative of a lower chance of a Type II error, which is the failure to reject a null hypothesis when it is actually false. Statistical power is defined as the probability that a test will correctly identify an effect when there is one, often determined by the sample size, the effect size, and the significance level.

When a study has higher power, it means that the test is more sensitive to detecting true effects, thus reducing the likelihood of incorrectly concluding that there is no effect (Type II error). Researchers aim for studies with high statistical power to ensure that if there is an actual effect, it can be reliably detected.

Other choices reflect different aspects of statistical analysis. The chance of Type I error refers to incorrectly rejecting a true null hypothesis, which power does not influence directly. Variability in data pertains to how spread out the data points are but doesn’t directly relate to statistical power. Additionally, the complexity of calculations does not inherently increase with higher power, as power primarily considers sample size and effect size without implying more complicated analysis.