How big does my sample need to be? A primer on the murky world of sample size estimation

Background: An explicit justification of sample size is now mandatory for most proposals submitted to funding bodies, ethics committees and, increasingly, for articles submitted for publication in journals. However, the process of sample size estimation is often confusing. Aim: Here, we present a primer of sample size estimation in an attempt to demystify the process. Method: First, we present a discussion of the parameters involved in power analysis and sample size estimation. These include the smallest worthwhile effect to be detected, the Types I and II error rates, and the variability in the outcome measure. Secondly, through a simplified, example 'dialogue', we illustrate the decision-making process involved in assigning appropriate parameter values to arrive at a ballpark figure for required sample size. We adopt a hypothetical, parallel-group, randomized trial design, though the general principles and concepts are transferable to other designs. The illustration is based on a traditional, power-analytic, null hypothesis-testing framework. In brief, we also address sample size estimation methods based on the required precision of the mean effect estimate. Conclusion: Rigorous sample size planning is important. Researchers should be honest and explicit regarding the decisions made for each of the parameters involved in sample size estimation.