Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145644
Title: Indices of effect existence and significance in the Bayesian framework
Authors: Makowski, Dominique
Ben-Shachar, Mattan S.
Chen, Annabel Shen-Hsing
Lüdecke, Daniel
Keywords: Social sciences::Psychology
Issue Date: 2019
Source: Makowski, D., Ben-Shachar, M. S., Chen, A. S.-H., & Lüdecke, D. (2019). Indices of effect existence and significance in the Bayesian framework. Frontiers in Psychology, 10, 2767-. doi:10.3389/fpsyg.2019.02767
Journal: Frontiers in Psychology
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.
URI: https://hdl.handle.net/10356/145644
ISSN: 1664-1078
DOI: 10.3389/fpsyg.2019.02767
Rights: © 2019 Makowski, Ben-Shachar, Chen and Lüdecke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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