For a particular study, power analysis can be approached in one of three ways:

1. If published data are available for the difference between two groups (or a treatment effect), the aim is to determine the number of subjects to be studied so that, if the groups differ by the published value, there will be stastistical significance most of the time (e.g., 80% of the time if power is 80%).

2. If it is possible to say what is the smallest difference (or treatment effect) of clinical interest (SDCI), the aim then is to determine the number of subjects to be studied so that there will be statistical significance if the true difference is at least SDCI. If the study is negative, one can conclude (with 80% confidence if the calculation is done at 80% power) that the difference (or treatment effect) if any is not of clinical interest. This approach can also be used for an equivalence study, where the sample size can be calculated for a difference below which two treatments can be considered equivalent.

3. If the number of subjects is fixed and known, as for instance in a retrospective study or in a time-bound prospective study, power analysis is used to determine the smallest difference (or treatment effect) for which the available number of subjects will lead (at the chosen power) to statistical significance.

Paired t-test (cross-over, each subject their own control)

Chi-square test (proportions in two groups)