Probably Overthinking It

“Downey presents a large assortment of graphs and numerical results drawn from legitimate databases and provides clear-cut examples to demonstrate how interpretive pitfalls arise. His style is lively and designed to appeal to the curious reader, and his choice of graphical formats skillfully illustrates his points. He explains challenging issues fully in a clear, logical manner.”  * Choice *“Each statistical paradox is illustrated using a cluster of interesting and concrete examples… . Apart from making abstract concepts easier to grasp, the examples and illustrations used help to train readers to identify statistical paradoxes. As Downey claims at several points in the book, once you understand a statistical paradox, you will see it everywhere.” * Metascience *“While it eschews the technical density of a textbook, it demands more intellectual engagement than a typical pop science book, drawing readers in with its broad scope of topics and colorful storytelling.” * Implicit Assumptions *“[Downey] employs principles of mathematics and statistics to give insights into everyday phenomena … None of my teachers ever explained how practical and relevant math can be for gaining, solving, and understanding real-world problems. How I wish Downey had been my teacher!” * Sekar Writes *“Downey’s pure love for the subject shines through abundantly, as does his social conscience and belief in the importance of statistical methods to illuminate the greatest, most challenging issues of our time.” – Aubrey Clayton, author of Bernoulli’s Fallacy: Statistical Illogic and the Crisis of Modern Science“Probably Overthinking It shows how fascinating and interesting statistics can be. Readers don’t need to be expert mathematicians. They just need to bring their curiosity about the world.” – Ravin Kumar, data scientist at Google“Probably Overthinking It is a delightful exposition of commonly-encountered statistical fallacies and paradoxes and why they matter. The illustrations are powerful and the prose is exceptionally clear. There are few domains of human activity to which the lessons of this volume are not applicable.” – Samuel H. Preston, coauthor of Demography: Measuring and Modeling Population Processes“Mark Twain once observed that ‘facts are stubborn things, but statistics are more pliable.’ Downey understands just how that happens, even to people who are not trying to obfuscate. It was an honest researcher who in 1971 found data that seemed to indicate smoking by pregnant women might be good for their babies—a misinterpretation that may have delayed anti-smoking measures by a decade. In this clear and cogent analysis, Downey explains why the data was misunderstood, as well as much else. It is a valuable book.” – Floyd Norris, Johns Hopkins University, former chief financial correspondent for the New York Times

Allen B. Downey is a curriculum designer at the online learning company Brilliant and professor emeritus of computer science at Olin College. He is the author of Think Python, Think Bayes, and Think Stats, among other books. He writes about statistics and related topics on his blog, Probably Overthinking It.