Current sample size conventions: flaws, harms, and alternatives

Abstract

Background: The belief remains widespread that medical research studies must have statistical power of at least 80% in order to be scientifically sound, and peer reviewers often question whether power is high enough.

Discussion: This requirement and the methods for meeting it have severe flaws. Notably, the true nature of how sample size influences a study's projected scientific or practical value precludes any meaningful blanket designation of <80% power as "inadequate". In addition, standard calculations are inherently unreliable, and focusing only on power neglects a completed study's most important results: estimates and confidence intervals. Current conventions harm the research process in many ways: promoting misinterpretation of completed studies, eroding scientific integrity, giving reviewers arbitrary power, inhibiting innovation, perverting ethical standards, wasting effort, and wasting money. Medical research would benefit from alternative approaches, including established value of information methods, simple choices based on cost or feasibility that have recently been justified, sensitivity analyses that examine a meaningful array of possible findings, and following previous analogous studies. To promote more rational approaches, research training should cover the issues presented here, peer reviewers should be extremely careful before raising issues of "inadequate" sample size, and reports of completed studies should not discuss power.

Summary: Common conventions and expectations concerning sample size are deeply flawed, cause serious harm to the research process, and should be replaced by more rational alternatives.

Figure 1

Qualitative depiction of how sample size influences a study's projected scientific and/or practical value. A threshold shaped relationship (dashed line) would create a meaningful distinction between adequate and inadequate sample sizes, but such a relation does not exist. The reality (solid line) is qualitatively different, exhibiting diminishing marginal returns. Under the threshold myth, cutting a sample size in half could easily change a valuable study into an inadequate one, but in reality such a cut will always preserve more than half of the projected value.