About the Book
Bayesian Statistics: The Basics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inference, contrasting it with classical statistical methods like null hypothesis significance testing. The book emphasizes key concepts such as prior and posterior distributions, Bayes’ theorem, and the Bayes factor, making them understandable even for readers with minimal mathematical backgrounds.
Methodologically, the book offers practical, step-by-step guides on how to conduct Bayesian analyses using the free software package JASP. Each chapter focuses on applying Bayesian methods to common research designs with real-world data. Readers will benefit from the clear examples, visualizations, and JASP screenshots that ensure the learning experience is interactive and easy to follow.
Full of practical content, the book emphasizes the advantages of Bayesian model comparison over traditional approaches, especially in quantifying evidence for competing hypotheses. Readers will also learn how to perform sensitivity analyses to assess the impact of different prior assumptions on their results.
By the end of the book, readers will get both the theoretical understanding and practical skills to implement Bayesian methods in their own research, making it an invaluable resource for both novice and experienced researchers studying Bayesian statistics.
Table of Contents:
Preface
1. Review of basic concepts
2. The language of Bayesian statistics
3. Bayesian correlation
4. The Bayesian t-test
5. Bayesian analysis of variance
6. Bayesian linear regression
7. Next steps and further reading
Glossary
Answers to selected end-of-chapter exercises
References
About the Author :
Thomas J. Faulkenberry, PhD, is a professor of psychological sciences and associate dean of the College of Graduate Studies at Tarleton State University in Stephenville, TX (USA). A mathematician by training, he teaches courses on statistics and mathematical modeling in the behavioral sciences, and his primary research areas are mathematical cognition and Bayesian statistics.
Review :
“Bayesian Statistics: The Basics provides extremely clear and accessible language to the novice, guiding the reader through Bayesian statistics with care, via hands-on examples involving user-friendly open-source software. Whether you are a graduate student, researcher, or faculty member, you now have no excuse for not deeply understanding, appreciating, and applying the fundamentals of Bayesian statistics.”
Fred Oswald, Professor of Psychological Sciences, Rice University, USA
“The book is written in a highly accessible manner, and what is most important to me is that it emphasizes the conceptual understanding behind Bayesian methods, which can be grasped even without a thorough understanding of underlying mechanics. I greatly appreciate the idea of a concise statistics book whose purpose is to build a solid conceptual framework that gives the reader a bird’s-eye view of the subject. I think Bayesian Statistics: The Basics serves this purpose brilliantly.”
Krzysztof Cipora, Senior Lecturer in Mathematical Cognition, Loughborough University, UK
“This is a highly accessible introduction to Bayesian statistics using JASP, a free, open-source program. The book features clear, concise explanations for beginners but will also provide new insights for people who already have a grasp of the basics. I particularly like that there are suggestions for how to report the results.”
Mark LaCour, Assistant Professor of Psychology, University of Louisiana at Lafayette, USA
“The book is an excellent introduction of both Bayesian statistics and JASP. Going through the examples in each chapter helped me understand both the theory and application simultaneously. I appreciated the stripped-down approach to covering the material with a very light focus on the underlying math. I now feel comfortable providing Bayesian results along with the classical results when I conduct experiments with these kinds of designs (e.g., needing correlation, t-test, ANOVA, or regression). Overall, I really enjoyed reading this book and it taught me a lot. I’m sure it will be popular and useful to many students, researchers, and faculty!”
Curt Carlson, Professor of Psychology, Texas A&M University – Commerce, USA
“Bayesian Statistics: The Basics is written in a clear, digestible tone that turns 'daunting' into 'doable.' This tone is then supported with a scaffolding approach, clear definitions, and comprehensive steps that takes Bayesian statistics from 'doable' to 'enjoyable.' This textbook is the reference I wish I had as a student, and the tool I can’t wait to use in the classroom.”
Bryanna Scheuler, PhD student in Psychology, The University of Texas at San Antonio, USA.
“Dr Faulkenberry’s writing style is so understandable and is great for students and researchers who are learning Bayesian statistics first the first time. One of my favorite parts of this book is the sample write-ups that are included at the end of each section. This ensures that researchers new to Bayesian statistics know how to properly communicate their Bayesian work in future publications. Using them as a model will help to make a Bayesian beginner write their statistics like a pro!”
Amy Bohmann, Associate Professor of Psychology, Texas A&M University – San Antonio, USA
