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Bayesian Statistics: The Basics
Bayesian 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.
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.
160 pages, Hardcover
Published April 29, 2025
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April 24, 2025
I received an evaluation copy of this book. It is the most approachable text on Bayesian statistics that I've read. If you're completely unfamiliar with Bayesian statistics, I'd recommend this as a good place to start. The author uses non-academic language to convey a conceptual understanding, and follows up with examples that are easy to follow using the free software JASP. If you have some experience with frequentist statistics and want to build on what you understand in a Bayesian framework, I would definitely recommend this book. In the last chapter, the author recommends where to go next to gain additional knowledge about Bayesian statistics after reading this book. Wow, I'm impressed!
Displaying 1 of 1 review