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On a generalizable approach for sample size determination in Bayesian t tests.

Tsz Keung Wong1, Jorge N Tendeiro2

  • 1Department of Methodology & Statistics, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands. t.k.wong3004@gmail.com.

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Summary
This summary is machine-generated.

Bayes factor design analysis (BFDA) offers a flexible approach for sample size determination in t-tests, moving beyond normality assumptions. A new method and Shiny app enhance its usability for researchers.

Keywords:
Bayes factorDesign analysisPower analysisSample size determination

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Area of Science:

  • Statistics
  • Bayesian Inference
  • Psychometrics

Background:

  • P-values are commonly used for null hypothesis testing, but Bayes factors offer a potentially superior alternative.
  • Bayes factor design analysis (BFDA) is crucial for optimizing study efficiency and informativeness.
  • Existing BFDA tools often rely on computationally intensive Monte Carlo methods.

Purpose of the Study:

  • To present a generalized method for conducting BFDA for sample size determination in t-tests.
  • To overcome limitations of existing BFDA methods by not assuming normality of effect size estimates.
  • To develop a user-friendly Shiny app to facilitate BFDA implementation.

Main Methods:

  • Developed a novel method for BFDA based on root-finding algorithms, generalizing existing approaches.
  • The method allows flexible specification of design and analysis priors without normality assumptions.
  • Created a Shiny application to demonstrate and apply the developed BFDA method.

Main Results:

  • The proposed method provides a flexible framework for BFDA in t-tests.
  • The Shiny app facilitates practical application of the BFDA method for sample size determination.
  • Exploration of Bayes factor operating characteristics under various prior specifications was conducted.

Conclusions:

  • The generalized BFDA method enhances the flexibility and applicability of Bayesian design analysis.
  • The user-friendly Shiny app promotes wider adoption of BFDA in research.
  • This work contributes to more robust and informative study designs using Bayesian approaches.