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

"Teacher, data scientist, and blogger Allen B. Downey knows well that the human mind has both an innate ability to understand statistics and to be fooled by them. Statistically speaking, you will be less popular than your friends, arrive at a train station during a gap in service, and fail to find a running mate in a race. But more than surprising us, errors in statistical thinking, Downey shows, can have a huge impact. Statistical confusion has led to incorrect patient prognoses, caused mistakes in predicting disasters like earthquakes, hurt vaccination programs, hindered social justice efforts, and led to dubious policy decisions. Written for those who may have once taken a statistics course, but now forget almost everything they've learned, the book includes a diversity of examples that use real data and have real world impacts. Building understanding incrementally, Downey engagingly and accessibly helps readers understand what we might learn when we get the mathematics right, and the consequences when we get it all wrong"--
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.