Statistical power is the likelihood that a test will be able to to detect an effect (during a research study) when one truly exists. When conducting a study, researchers are essentially trying to find out if their hypothesis is correct. But in statistics, we don’t go about trying to confirm our hypothesis. Instead, we test the opposite of our hypothesis, called the null hypothesis, by looking for enough evidence to say that it is false, and we should reject it. In rejecting the null hypothesis, we are in effect saying that our hypothesis is true. In other words, Statistical Power is the probability of correctly rejecting the null hypothesis when it is in fact false (meaning, the original hypothesis is true). I know, sounds confusing, but hang with me. Let’s look at an example.
Let’s say you want to find out if taking a vitamin supplement increases mental alertness. And let’s say that, in this instance, the vitamin supplement is indeed effective in increasing alertness. Your test would have Statistical Power if it is able to lead you to correctly reject the null hypothesis.
If a test has high Statistical Power, then it will help you to conclude that the vitamin supplement has an effect. If a test lacks Statistical Power, you might end up wrongly concluding that the vitamin supplement is useless in increasing alertness, when in fact it is effective.