Bayesian data analysis for newcomers

Brief Report

DOI: 10.3758/s13423-017-1272-1

Cite this article as:
Kruschke, J.K. & Liddell, T.M. Psychon Bull Rev (2017). doi:10.3758/s13423-017-1272-1


This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.


Bayesian model Bayesian analysis Null hypothesis significance test Bayes factor Highest density interval Region of practical equivalence p value Confidence interval Replication crisis 

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA

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