The equivalence of two approaches to incorporating variance uncertainty in sample size calculations for linear statistical models

Abstract

Sample size determination is one of the most commonly encountered tasks in the design of every applied research. The general guideline suggests that a pilot study can offer plausible planning values for the vital model characteristics. This article examines two viable approaches to taking into account the imprecision of a variance estimate in sample size calculations for linear statistical models. The multiplier procedure employs an adjusted sample variance in the form of a multiple of the observed sample variance. The Bayesian method accommodates the uncertainty of a sample variance through a prior distribution. It is shown that the two seemingly distinct techniques are equivalent for sample size determination under the designated assurance requirements that the actual power exceeds the planned threshold with a given tolerance probability, or the expected power attains the desired level. The selection of optimum pilot sample size for minimizing the expected total cost is also considered.