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Indices of Effect Existence and Significance in the Bayesian Framework.

Authors
  • Makowski, Dominique1
  • Ben-Shachar, Mattan S2
  • Chen, S H Annabel1, 3, 4
  • Lüdecke, Daniel5
  • 1 School of Social Sciences, Nanyang Technological University, Singapore, Singapore. , (Singapore)
  • 2 Department of Psychology, Ben-Gurion University of the Negev, Beersheba, Israel. , (Israel)
  • 3 Centre for Research and Development in Learning, Nanyang Technological University, Singapore, Singapore. , (Singapore)
  • 4 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore. , (Singapore)
  • 5 Department of Medical Sociology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. , (Germany)
Type
Published Article
Journal
Frontiers in Psychology
Publisher
Frontiers Media SA
Publication Date
Jan 01, 2019
Volume
10
Pages
2767–2767
Identifiers
DOI: 10.3389/fpsyg.2019.02767
PMID: 31920819
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Turmoil has engulfed psychological science. Causes and consequences of the reproducibility crisis are in dispute. With the hope of addressing some of its aspects, Bayesian methods are gaining increasing attention in psychological science. Some of their advantages, as opposed to the frequentist framework, are the ability to describe parameters in probabilistic terms and explicitly incorporate prior knowledge about them into the model. These issues are crucial in particular regarding the current debate about statistical significance. Bayesian methods are not necessarily the only remedy against incorrect interpretations or wrong conclusions, but there is an increasing agreement that they are one of the keys to avoid such fallacies. Nevertheless, its flexible nature is its power and weakness, for there is no agreement about what indices of "significance" should be computed or reported. This lack of a consensual index or guidelines, such as the frequentist p-value, further contributes to the unnecessary opacity that many non-familiar readers perceive in Bayesian statistics. Thus, this study describes and compares several Bayesian indices, provide intuitive visual representation of their "behavior" in relationship with common sources of variance such as sample size, magnitude of effects and also frequentist significance. The results contribute to the development of an intuitive understanding of the values that researchers report, allowing to draw sensible recommendations for Bayesian statistics description, critical for the standardization of scientific reporting. Copyright © 2019 Makowski, Ben-Shachar, Chen and Lüdecke.

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