ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2018, Vol. 26 ›› Issue (6): 951-965.doi: 10.3724/SP.J.1042.2018.00951

• Research Method •     Next Articles

The Bayes factor and its implementation in JASP: A practical primer

HU Chuan-Peng1,2(), KONG Xiang-Zhen3, Eric-Jan WAGENMAKERS4, Alexander LY4,5, PENG Kaiping1()   

  1. 1 Department of Psychology, School of Social Science, Tsinghua University, Beijing 100084, China
    2 Neuroimaging Center, Johannes Gutenberg University Medical Center, 55131 Mainz, Germany
    3 Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
    4 Department of Psychological Methods, University of Amsterdam, 1018 VZ Amsterdam, The Netherlands
    5 Centrum Wiskunde & Informatica, 1090 GB Amsterdam, The Netherlands
  • Received:2017-10-10 Online:2018-06-10 Published:2018-04-28
  • Contact: Chuan-Peng HU,Kaiping PENG;


Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0 and the alternative hypothesis H1 based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for both the H0 and the H1, it is not “violently biased” against H0, it allows one to monitor the evidence as the data accumulate, and it does not depend on sampling plans. Importantly, the recently developed open software JASP makes the calculation of Bayes factor accessible for most researchers in psychology, as we demonstrated for the t-test. Given these advantages, adopting the Bayes factor will improve psychological researchers’ statistical inferences. Nevertheless, to make the analysis more reproducible, researchers should keep their data analysis transparent and open.

Key words: Bayes factor, Bayesian statistics, Frequentist, NHST, JASP

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