John K. Kruschke is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics, at Indiana University in Bloomington, Indiana, USA. He is eight-time winner of Teaching Excellence Recognition Awards from Indiana University. He won the Troland Research Award from the National Academy of Sciences (USA), and the Remak Distinguished Scholar Award from Indiana University. He has been on the editorial boards of various scientific journals, including Psychological Review, the Journal of Experimental Psychology: General, and the Journal of Mathematical Psychology, among others.

After attending the Summer Science Program as a high school student and considering a career in astronomy, Kruschke earned a bachelor's degree in mathematics (with high distinction in general scholarship) from the University of California at Berkeley. As an undergraduate, Kruschke taught self-designed tutoring sessions for many math courses at the Student Learning Center. During graduate school he attended the 1988 Connectionist Models Summer School, and earned a doctorate in psychology also from U.C. Berkeley. He joined the faculty of Indiana University in 1989. Professor Kruschke's publications can be found at his Google Scholar page. His current research interests focus on moral psychology.

Professor Kruschke taught traditional statistical methods for many years until reaching a point, circa 2003, when he could no longer teach corrections for multiple comparisons with a clear conscience. The perils of p values provoked him to find a better way, and after only several thousand hours of relentless effort, the 1st and 2nd editions of Doing Bayesian Data Analysis emerged.

1. What's in This Book (Read This First!) PART I The Basics: Models, Probability, Bayes' Rule, and R2. Introduction: Credibility, Models, and Parameters
3. The R Programming Language
4. What Is This Stuff Called Probability?
5. Bayes' Rule PART II All the Fundamentals Applied to Inferring a Binomial Probability6. Inferring a Binomial Probability via Exact Mathematical Analysis
7. Markov Chain Monte Carlo
8. JAGS
9. Hierarchical Models
10. Model Comparison and Hierarchical Modeling
11. Null Hypothesis Significance Testing
12. Bayesian Approaches to Testing a Point ("Null") Hypothesis
13. Goals, Power, and Sample Size
14. Stan PART III The Generalized Linear Model15. Overview of the Generalized Linear Model
16. Metric-Predicted Variable on One or Two Groups
17. Metric Predicted Variable with One Metric Predictor
18. Metric Predicted Variable with Multiple Metric Predictors
19. Metric Predicted Variable with One Nominal Predictor
20. Metric Predicted Variable with Multiple Nominal Predictors
21. Dichotomous Predicted Variable
22. Nominal Predicted Variable
23. Ordinal Predicted Variable
24. Count Predicted Variable
25. Tools in the Trunk