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Related Experiment Videos

Bayesian evaluation of effect size after replicating an original study.

Robbie C M van Aert1, Marcel A L M van Assen1,2

  • 1Department of Methodology and Statistics, Tilburg University, the Netherlands.

Plos One
|April 8, 2017
PubMed
Summary
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Many published studies lack reliability, as shown by replication projects in psychology and economics. A new Bayesian method, snapshot hybrid, quantifies evidence for various effect sizes, addressing limitations of traditional significance testing.

Area of Science:

  • Psychology
  • Economics
  • Bayesian Statistics
  • Meta-Analysis

Background:

  • A high proportion of statistically significant published results raises concerns about scientific reliability.
  • Replication projects in psychology (RPP) and economics (EE-RP) revealed low replication rates and suggested numerous null effects.
  • Null hypothesis significance testing cannot adequately assess evidence for the null hypothesis.

Purpose of the Study:

  • To develop a user-friendly Bayesian meta-analysis method, snapshot hybrid, for quantifying evidence of zero, small, medium, and large effects.
  • To adjust for publication bias by considering the statistical significance of original studies.
  • To provide a tool for determining necessary sample sizes for replications, similar to power analysis.

Main Methods:

Related Experiment Videos

  • Developed the snapshot hybrid Bayesian meta-analysis method.
  • Computed posterior model probabilities for different effect size categories (zero, small, medium, large).
  • Analytically approximated the method's performance and applied it to RPP and EE-RP data.

Main Results:

  • The snapshot hybrid method quantifies evidence for various effect sizes and accounts for publication bias.
  • EE-RP studies generally exhibit larger effect sizes than RPP studies.
  • RPP studies often have insufficient sample sizes for definitive conclusions on true effect sizes.

Conclusions:

  • The snapshot hybrid method offers a robust approach to evaluating evidence for effect sizes, including the null effect.
  • Replication studies highlight the need for adequate sample sizes to draw reliable conclusions.
  • The developed method and associated tools (web application, R code) facilitate robust meta-analysis and replication planning.