Optimal Sample Sizes for Testing the Equivalence of Two Means

Jiin Huarng Guo, Hubert J. Chen, Wei Ming Luh

Research output: Contribution to journalArticlepeer-review


Equivalence tests (also known as similarity or parity tests) have become more and more popular in addition to equality tests. However, in testing the equivalence of two population means, approximate sample sizes developed using conventional techniques found in the literature on this topic have usually been under-valued as having less statistical power than is required. In this paper, the authors first address the reason for this problem and then provide a solution using an exhaustive local search algorithm to find the optimal sample size. The proposed method is not only accurate but is also flexible so that unequal variances or sampling unit costs for different groups can be considered using different sample size allocations. Figures and a numerical example are presented to demonstrate various configurations. An R Shiny App is also available for easy use (https://optimal-sample-size.shinyapps.io/equivalence-of-means/).

Original languageEnglish
Pages (from-to)128-136
Number of pages9
Issue number3
Publication statusPublished - 2019 Aug 29

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Psychology(all)

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