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This study introduces a Bayesian method to accurately estimate both effect size and prevalence in behavioral science research. This improves understanding of the "typical" person by accounting for individual differences.

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Area of Science:

  • Behavioral Sciences
  • Psychological Research Methods
  • Statistical Modeling

Background:

  • Group-averaged effect size is often misinterpreted as representing the typical individual.
  • This interpretation relies on unstated distributional assumptions.
  • Mean effect size is influenced by within-participant effects and population prevalence.

Purpose of the Study:

  • To develop a method that jointly estimates prevalence and effect size.
  • To address limitations of existing prevalence estimation methods confounded by effect size uncertainty.
  • To improve characterization of the 'typical' person in behavioral science.

Main Methods:

  • Introduction of a Bayesian p-curve mixture model.
  • Probabilistic clustering of participant-level data based on null distribution likelihood.
  • Development of a supporting software tool.

Main Results:

  • The proposed method jointly estimates prevalence and effect size.
  • Outperforms existing prevalence estimation methods when effect size is uncertain.
  • Demonstrates sensitivity to prevalence or effect size differences across groups/conditions.

Conclusions:

  • The Bayesian p-curve mixture model offers a robust approach to estimating prevalence and effect size.
  • This method enhances the accuracy of characterizing typical effects in behavioral science.
  • The approach provides a valuable tool for researchers dealing with uncertain effect sizes and varying prevalence.