Replication study design: confidence intervals and commentary

Lawrence L. Kupper () and Sandra L. Martin
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Lawrence L. Kupper: University of North Carolina
Sandra L. Martin: University of North Carolina

Statistical Papers, 2022, vol. 63, issue 5, No 9, 1577-1583

Abstract: Abstract Methods for designing a comparable replication study have received considerable attention in the published literature, with both Bayesian and non-Bayesian methods having been developed from a hypothesis testing and associated P-value perspective. The purpose of this paper is to describe, using a maximum likelihood-based confidence interval framework, a new frequentist method for choosing the sample size for a comparable replication study. This new method is compared to the published “predictive power” (or “PP”) method. For each of these two methods, a new and easy-to-use formula is derived for computing the optimal comparable replication study sample size that guarantees satisfying a specific confidence interval criterion with a chosen high minimum probability. Connections to hypothesis testing are made, and the Discussion section provides further commentary and considers a numerical example involving published data.

Keywords: Replication crisis; Comparable replication study; Confidence intervals; Predictive power; 62 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00362-022-01291-2

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