Conventional wisdom dictates that it is unethical to conduct a study that is so large that excess numbers of patients are exposed or so small that clinically important changes cannot be detected.1 This implies that there is some optimal sample size that can be calculated using statistical theory and information from previous research. But the choice of sample size is usually a compromise between statistical considerations, which always benefit from increased sample size, and economic or logistical constraints.2

Only rarely is sufficient information available to make informed decisions. Moreover, despite the illusion of precision that arises from the application of arcane statistical formulas, in many situations the choice of inputs—the expected treatment effect, the standard deviation, and the power—are subject to considerable uncertainty. As a result, sample size calculations may vary widely.

We argue that, in the absence of good data, it would be better to determine sample size by adopting norms derived from historical data based on large numbers of studies of the same type. We show that for many common situations we can define defensible, evidence based, ranges of sample sizes.