Power is defined as the probability that a hypothesis test will reject, given that the hypothesis is false. Since the objective of many projects is to establish a difference between treatments by rejection of the hypothesis of equal treatment means, power often represents the probability of success of the experiment. Power calculations are an important aid in determining the number of subjects that will be sufficient to meet the objective, but will not be wasteful of animals or resources. Power calculations are also important when the objective of research is to establish the lack of an effect. If a study fails to detect a difference between two treatment means, before concluding that that no difference exists, one must determine that the experiment had enough power to have detected a difference.
Researchers generally consider power calculations to be difficult.
With the available software and a little bit of training, the power computation itself is not difficult for most basic analyses. The difficulty lies in the fact that the true power depends on quantities that are unknown. The probability of detecting a significant difference depends on the size of the true difference relative to the amount of variability in the individual observations. In practice, the researcher often must proceed using poor estimates, or wild guesses, of these quantities. Because the results are only as good as the inputs, we often do several calculations with hypothetical values to get an idea how power estimates vary over a reasonable range of input values.