ISSN 2043-8087
Journal of Experimental Psychopathology
 Vol. In Press
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A Primer on Bayesian Analysis for Experimental Psychopathologists

  Angelos-Miltiadis Krypotos - Department of Clinical Psychology, Utrecht Univers
  Tessa Blanken - Department of Sleep and Cognition, Netherlands Ins
  Inna Arnaudova - Department of Clinical Psychology, University of A
  Dora Matzke - Department of Psychological Methods and Statistics
  Tom Beckers - Department of Clinical Psychology, University of A

In Press (Uncorrected Proof), Pages 1-20


The principal goals of experimental psychopathology (EPP) are to offer insights into the pathogenic mechanisms of mental disorders and to provide a stable ground for the development of clinical interventions. The main message of the present article is that those goals are better served by the adoption of Bayesian statistics than by the continued use of Null-Hypothesis Significance Testing (NHST). In the first part of the article we list the main disadvantages of NHST and explain why those disadvantages limit the conclusions that can be drawn from EPP research. Next, we highlight the advantages of Bayesian statistics. To illustrate, we then pit NHST and Bayesian analysis against each other using an experimental data set from our lab. Finally, we discuss some challenges when adopting Bayesian statistics. We hope that the present article will encourage experimental psychopathologists to embrace Bayesian statistics, which could strengthen the conclusions drawn from EPP research.

Table of Contents

Correspondence to
Dr. Angelos-Miltiadis Krypotos

p-values, Bayesian inference, statistical analysis, mental disorders, fear learning

Received 26 Mar 2016; Revised 1 Dec 2016; Accepted 1 Dec 2016; In Press 19 Feb 2017

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