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

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.

Table of Contents:
Introduction 1. Are You Normal? Hint: No 2. Relay Races and Revolving Doors 3. Defy Tradition, Save the World 4. Extremes, Outliers, and GOATs 5. Better Than New 6. Jumping to Conclusions 7. Causation, Collision, and Confusion 8. The Long Tail of Disaster 9. Fairness and Fallacy 10. Penguins, Pessimists, and Paradoxes 11. Changing Hearts and Minds 12. Chasing the Overton Window Epilogue Acknowledgments Bibliography Index

About the Author :
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 :
“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.”  “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.” “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.” “[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!” “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.” “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.” “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.” “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.”