Background
When calculating the required sample size for a definitive randomized controlled trial, an assumed benefit (that is, the mean improvement in outcomes due to the intervention) must be specified. In broad terms, the main approaches to setting the assumed benefit include (1) select a value for Cohen’s (standardized) effect size, (2) use an estimate obtained from previous studies (possibly pilot studies), (3) elicit a value from experts, (4) use a value that is considered a realistic benefit, and (5) use a value that represents the minimum important difference (MID). What constitutes a MID often is not well-defined, but criteria typically are anchored to the notions of the smallest benefit that would be of interest to stakeholders or the benefit that would need to be observed to justify a change in practice. The minimum clinically important difference (MCID) (or simply clinically important difference (CID), when the connection with a minimum value is implied) often is used synonymously with the MID. This commentary does not summarize the extensive literature discussing the advantages and shortcomings of the various approaches, but instead focuses on the (mis-)use of the MID and the central role of the realistic benefit. Unless noted otherwise, it is assumed throughout that the null hypothesis being tested is that the true benefit is zero.The DELTA