ISSN 2043-8087
Journal of Experimental Psychopathology
 Volume 8, Issue 2, 140-157, 2017
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A Primer on Bayesian Analysis for Experimental Psychopathologists

Authors
  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

Volume 8, Issue 2, 2017, Pages 140-157
DOI: http://dx.doi.org/10.5127/jep.057316

Abstract

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
Introduction
 Why NHST is ill-suited for EPP research
 Bayesian analysis as an alternative to NHST or How I learned to stop worrying and love the data
 Bayesian parameter estimation
 Bayesian hypothesis testing
 Advantages of Bayesian analysis over NHST
 Pitting inferences from NHST and Bayesian hypothesis testing against each other: An example from the field of EPP
Methods
 Participants.
 Procedure.
 Habituation phase.
 Acquisition phase.
 Test phase.
 Exit Interview and Questionnaires.
 Data Analyses.
Results
 Questionnaires and Evaluations.
 US-expectancy Ratings.
 FPS results.
 Experimental Conclusions.
Discussion
Acknowledgements
References

Correspondence to
Dr. Angelos-Miltiadis Krypotos, Department of Clinical Psychology, Utrecht University, Utrecht, the Netherlands.

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

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









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