Why you don’t need to be a statistician to think like one

A fire hose of information bombards us every day, and some of it is even true. Is Bitcoin a good investment? Are hurricanes getting worse? Is the measles vaccine dangerous? Separating the wheat from the chaff is what statisticians do—and there’s lots of chaff. To Think Like a Statistician shares the skills statisticians use to sift through evidence, learn from experience, and extract meaning and knowledge from the random and the contradictory.

Bradley Efron is one of the most renowned statisticians in the world and has shaped how data science and machine learning are practiced today. In this book, he draws on examples ranging from David Hume’s critique of miracles to counterfeit Basquiats, AI hallucinations, pandemics, competing political claims, government approvals of Alzheimer’s treatments, gambling, and misinformation. He describes how statisticians have tackled difficult topics—like correlation, causation, prediction, survival, and accuracy—and demystifies the disputes surrounding concepts like randomness, uncertainty, and subjectivity.

Blending real-world insights with personal stories from a leading expert, To Think Like a Statistician equips readers with powerful ideas from the statistician’s toolbox and explains the tricks of the trade, enabling anyone to become a more sophisticated consumer of information in an increasingly noisy world.
 
Contributor Bio(s)
 
Bradley Efron is the Max H. Stein Professor Emeritus of Statistics and Biomedical Data Science at Stanford University and a recipient of the National Medal of Science. His books include Exponential Families in Theory and Practice and Large-Scale Inference. He is a MacArthur Fellow and member of the National Academy of Sciences.