Power – (1-beta), where beta is the risk of a type 2 error – rejecting the accepting the null hypothesis when it is true – this is usually selected to be 0.8 or 0.9.
Significance (alpha), the chance of making a type 1 error – accepting the alternate hypothesis when the null hypothesis is true. This is usually selected to be 0.05.
One-tailed or two-tailed – is the null hypothesis one of no difference (experimental arm not better or worse) or one-sided (experimental arm not better)?
Effect Size. This is the challenging part. This is the size of the difference in outcomes you’re looking for.
For continuous outcomes (example – difference in pain scores). You’ll need an estimate for the variation in the scores between presentations, or the standard deviation. You can get this from a literature estimate or a from small local measurement, say of 10 patients or so.
For a dichotomous outcome (example – percentage of successes), you can usually estimate the percentage in one group and choose the difference you are looking for.
The effect size has a big effect on the sample size. Generally, cutting the effect size in half increases the sample size by fourfold.
