A Primer on Bayesian Analysis for Experimental Psychopathologists

Angelos Miltiadis Krypotos*, Tessa F. Blanken, Inna Arnaudova, Dora Matzke, Tom Beckers

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


The principal goals of experimental psychopathology (EPP) research 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.

Original languageEnglish
Pages (from-to)140-157
Number of pages18
JournalJournal of Experimental Psychopathology
Issue number2
Publication statusPublished - 1 Jun 2017
Externally publishedYes


  • Bayesian inference
  • fear learning
  • mental disorders
  • NHST
  • statistical analysis


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