Overview

With this book, Will Kurt provides a friendly introduction to Bayesian statistics. He introduces topics with stories rather than formulae, and the reader needs only basic algebra and common intuition to follow along. After finishing the book, you will have a stronger intuition behind basic probability and some introductory skills in practical statistics.

I read this book before taking classes in time series analysis and measure-theoretic probability and found it helpful in the beginning.

Who Should Read This Book

• Beginners in probability and statistics who want to develop their intuition and understanding of Bayesian thinking.

• Anyone curious about practical, real-world applications of probability without getting lost in mathematical rigor.

Contents

The book begins with gentle slope into basic probability and the core Bayesian notion that all probabilities are conditional (on what you already know) and that new evidence merely updates your prior. If you haven't thought about probability like this before then this can be quite a powerful way of feeling things intuitively, especially when it comes to things like probability paradoxes.

After the first half, you will have covered some basic probability distributions, conditional probability, and Bayes' theorem. If you've already got some background in prob & stats or you're quite mathematically inclined, then this can feel quite slow, but still may provide new intuitive ways of thinking about things.

The second half covers the more practical side of parameter estimation and hypothesis testing. Again, these topics aren't inherently that complicated and so may not benefit from a slow, 100-page draw out into it, but like the first half, there are a few novel nuggets of information that are worth digging for, especially if you haven't covered these topics before.

About the Author

The author is an American data scientist who runs a blog at countbayesie.com. He actually made this book mostly from blog posts he had already made on probability and statistics.

3 I Liked

1. Accessible writing. He uses a friendly, conversational tone that makes complex concepts approachable. Topics that often intimidate beginners are explained using clear, real-world analogies (e.g., dice, candy, sports).
2. Hands-on approach. For those who learn by doing, this book is for you. He includes numerous exercises and Python code snippets, so you can immediately test your understanding and see Bayesian reasoning in action.
3. Emphasis on intuition. The book consistently reinforces "why" over "how". He explains not just the maths but also the reasoning behind Bayesian thinking: why updating beliefs with evidence is powerful, and how it differs from classical statistics.

3 I didn't like

1. Lack of mathematical depth. Not sure if this is even a fair criticism as this is NOT what the book is for but advanced readers or statisticians may find the explanations somewhat light on formal proofs and general mathematical rigor. It’s great for intuition but not a comprehensive reference. Don't treat this as a textbook, treat it as a primer.
2. Not as concise as I'd like. Like almost all books, the author could have gotten his main points across in far fewer words, but for readers who are comfortable skim-reading or who have lots of time, this isn't much of a problem.
3. Not as visual as I'd like. For a book that doesn't use a lot of mathematics, I think it would have benefited from more diagrams, charts, and pictures to explain things. For the visual readers among you, you'd probably be better off finding a good video series.

Final Takeaway

Bayesian Statistics the Fun Way is exactly what it promises: a fun, approachable, and practical guide to Bayesian reasoning. It’s a great entry point for beginners and a gentle refresher for anyone interested in probabilistic thinking.