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Probably Overthinking It: How to Use Data to Answer Questions, Avoid Statistical Traps, and Make Better Decisions

Contributor(s): Downey, Allen B (Author)

ISBN: 9780226845555

Publisher: University of Chicago Press

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Pub Date: December 4, 2025

Lexile Code: 0000

Features: Price on Product

Target Age Group: NA to NA

Physical Info: 0.58" H x 8.99" L x 6.09" W ( 0.81 lbs) 256 pages

Descriptions, Reviews, etc.

Description: An essential guide to the ways data can improve decision making.

Statistics are everywhere: in news reports, at the doctor's office, and in every sort of forecast, from the stock market to the weather. Blogger, teacher, and computer scientist Allen B. Downey knows well that people have an innate ability both to understand statistics and to be fooled by them. As he makes clear in this accessible introduction to statistical thinking, the stakes are big. Simple misunderstandings have led to incorrect medical prognoses, underestimated the likelihood of large earthquakes, hindered social justice efforts, and resulted in dubious policy decisions. There are right and wrong ways to look at numbers, and Downey will help you see which are which.

Probably Overthinking It uses real data to delve into real examples with real consequences, drawing on cases from health campaigns, political movements, chess rankings, and more. He lays out common pitfalls--like the base rate fallacy, length-biased sampling, and Simpson's paradox--and shines a light on what we learn when we interpret data correctly, and what goes wrong when we don't. Using data visualizations instead of equations, he builds understanding from the basics to help you recognize errors, whether in your own thinking or in media reports. Even if you have never studied statistics--or if you have and forgot everything you learned--this book will offer new insight into the methods and measurements that help us understand the world.

Brief description: 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.

Review Quotes: "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"

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