Spiegelhalter (Univ. of Cambridge) here offers a thoughtful introduction to statistics and data analysis with potential appeal for a wide audience. Topics addressed include distinguishing between effective and ineffective chart and graph implementations, with a thorough description of regression analysis, a deep dive into causation, an overview of artificial intelligence and prediction tools, and a more detailed look at causation and probability considered together. Examples of various statistical methods are interesting because they are based on situations familiar in daily life. Each chapter concludes with a bullet-point list of key concepts covered, a potential help especially for readers new to the processes and methods involved. The book's glossary, index, and notes also provide helpful context for the target reader. The book is suitable for undergraduates and anyone seeking to improve their statistical literacy. The author's aim is clearly to improve readers' understanding of statistics and what they mean, without requiring them to actually perform statistical calculations. While of particular value for those who hope to study or work in data science or adjacent fields, this is a highly engaging work that will be valued by many readers in today's data-driven academic environment. Summing Up: Highly recommended. Lower- and upper-division undergraduates. Students in two-year technical programs. General readers. --Kristin Jan Whitehair, independent scholar

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. (Sept.)

An exploration of "why we need statistics" and how to use them effectively.The fact that Darrell Huff's delightful How to Lie With Statistics (1954) remains in print should convince readers that politicians, demagogues, and advertisers have never had trouble misleading us with numbers and graphs. Still, the study of statistics is widely considered boring, so popular books on the subject work hard to be entertaining; this expert primer mostly measures up. Distinguished British statistician Spiegelhalter (Statistics/Univ. of Cambridge; Sex by Numbers, 2015, etc.), a former president of the Royal Statistical Society, writes that "numbers do not speak for themselves; the context, language, and graphic design all contribute to the way communication is received. We have to acknowledge that we are telling a story." Some statistics are meaninglesse.g., based on average, a human has one testicle. Some are unhelpful: Vegetarians earn more than meat-eaters, but avoiding meat is unlikely to boost your income. An identical statistic can tell a horror storye.g., a drug increases the risk of lung cancer by 14%, or not, if it increases the risk from 1 to 1.14 in 1,000. Unlike Huff's slim volume, Spiegelhalter goes beyond debunking numerical nonsense to deliver a largely mathematics-free but often formidable education on the vocabulary and techniques of statistical science. Almost everyone will understand how "median" differs from "average," and most will grasp the meaning of a bell curve or that "deduction" (using the rules of logic to come to a conclusion, Sherlock Holmes) is the converse of "induction" (using particular events to draw a general conclusion). Despite careful explanations and a plethora of tables and graphs, readers may strain to understand concepts such as the Poisson distribution, confidence intervals, bootstrapping, or standard deviation, but their efforts will be rewarded.An admirable corrective to fake news and sloppy thinking. Copyright Kirkus Reviews, used with permission.