The definitive guide to statistical thinking
Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders.
In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive.
Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.
Spiegelhalter (Sex by Numbers), a University of Cambridge statistician, demonstrates in his intriguing, nontechnical primer how to reliably evaluate even the most extravagant claim. Spiegelhalter's goal is to show readers that statistics is about more than just counting numbers. A question about what happened to children having heart surgery at a particular hospital becomes a lesson in the psychological effects of "framing" results: reporting the "mortality rate" might cause alarm, but providing a "survival rate" sounds more reassuring. From issues with pie charts and the "wisdom of crowds," to using data distributions, modelling relationships, and the correlation/causation quandary, Spiegelhalter offers clear and surprisingly enlightening examples. Concepts including margins of error and statistical significance, he demonstrates, become vital when assessing a statistics-backed claim, such as one made by a mischievous journalist who published a paper "proving" chocolate consumption caused weight loss the data was real, but any trained statistician could see it was statistically insignificant. Spiegelhalter's book is both fully comprehensible and valuable in a digitally driven world in which data literacy has become newly important.