The correct answer is False. Power is the probability of rejecting the null hypothesis when it is false.
The null hypothesis is a statement about the population that is being tested. The alternative hypothesis is the statement that is being tested against the null hypothesis. The power of a test is the probability of rejecting the null hypothesis when the alternative hypothesis is true.
In other words, power is the probability of correctly identifying a difference when there actually is a difference. A high power means that the test is likely to detect a difference when there is one, while a low power means that the test is likely to miss a difference when there is one.
Power is affected by several factors, including the size of the effect, the sample size, and the significance level. The larger the effect, the easier it is to detect. The larger the sample size, the more power the test has. The lower the significance level, the more power the test has.
Power is an important consideration in the design of a study. A study with low power is more likely to miss a difference when there is one. This can lead to false-negative results, which can have serious consequences. Therefore, it is important to design studies with adequate power to detect the differences that are of interest.