Why you don’t need to be a statistician to think like oneA 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 know...

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian an...

An Introduction to the Bootstrap arms scientists and engineers as well as statisticians with the computational techniques they need to analyze and understand complicated data sets. The bootstrap is a computer-based method of statistical inference that answers statistical questions without formulas and gives a direct appreciation of variance, bias, coverage, and other probabilistic phenomena. This book presents an overview of the bootstrap and related methods for assessing statistical accur...

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computati...

This textbook systematically presents fundamental methods of statistical analysis: from probability and statistical distributions, through basic concepts of statistical inference, to a collection of methods of analysis useful for scientific research. It is rich in tables, diagrams, and examples, in addition to theoretical justification of the methods of analysis introduced. Each chapter has a section entitled “Exercises and Problems” to accompany the text. There are altogether about 300 ex...

**NATIONAL BESTSELLER • From the classroom to the courtroom and from financial markets to supermarkets, an intriguing and illuminating look at how randomness, chance, and probability affect our daily lives that will intrigue, awe, and inspire.“Mlodinow writes in a breezy style, interspersing probabilistic mind-benders with portraits of theorists.... The result is a readable crash course in randomness.” —The New York Times Book Review**With the born storyteller's co...

"A fascinating description of the kinds of people who interacted, collaborated, disagreed, and were brilliant in the development of statistics." —Barbara A. Bailar, Senior Vice-President, National Opinion Research CenterAt a summer tea party in Cambridge, England, a guest states that tea poured into milk tastes different from milk poured into tea. Her notion is shouted down by the scientific minds of the group. But one man, Ronald Fisher, proposes to scientifically...

**“Witty, compelling, and just plain fun to read . . ." —Evelyn Lamb, Scientific AmericanThe Freakonomics of math—a math-world superstar unveils the hidden beauty and logic of the world and puts its power in our hands**The math we learn in school can seem like a dull set of rules, laid down by the ancients and not to be questioned. In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract...

'A statistical national treasure' Jeremy Vine, BBC Radio 2'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - ...

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal...

An argument for how understanding causality has revolutionized science and will revolutionize artificial intelligence.**"Illuminating." —**New York Times**"Extraordinary." —**Science Friday“Correlation is not causation.” This mantra, chanted by scientists for more than a century, once led to a virtual prohibition on causal talk. Today, with causal analysis taking center stage in AI and other applic...

**Winner of the Mavis Gallant Prize for Non-FictionWinner of the 2017 National Business Book AwardShortlisted for the 2016/2017 Donner Prize**From the bestselling author of The Organized Mind, the must-have book about how to analyze who and what to trust in the age of information overload.It's becoming harder to separate the wheat from the digital chaff. How do we distinguish misinformation, pseudo-facts, distortions and outright li...