What does "statistical power" refer to in hypothesis testing?

Statistical power is fundamentally the probability that a test will correctly reject a false null hypothesis. This concept is crucial in hypothesis testing because it informs researchers about the likelihood of detecting an effect when there truly is one. High power, which is typically deemed acceptable when above 0.8 (or 80%), means that there is a strong chance of identifying true effects, reducing the risk of a Type II error, where a false null hypothesis fails to be rejected.

In practical terms, statistical power can be influenced by several factors, including the sample size, the effect size, and the significance level set for the test. A larger sample size generally increases power because it provides a more accurate estimate of the population parameter and reduces variability in the test statistic. Additionally, when the effect size is large, it is easier to detect significant differences, thus enhancing power.

Although the remaining options relate to hypotheses and their testing, they do not encapsulate the essence of statistical power. For instance, making a Type I error involves incorrectly rejecting a true null hypothesis, which is separate from the concept of power. The likelihood of obtaining a statistically significant result ties closely with power, but it does not define it precisely; statistical power is concerned explicitly with the correct rejection of a false