Learn how data literacy is changing the world and giving you a better understanding of life's biggest problems in this "Important and Comprehensive" Guide to Statistical Thinking (New York). The big data era has made statistical knowledge more important than ever. In The Art of Statistics, David Spiegelhalter shows how to apply statistical arguments to real-world problems. Whether it's preventive medical exams or the terrible criminal cases of serial killers - Spiegelhalter teaches us to clarify doubts, assumptions and expectations and, above all, to interpret the answers. The unrivaled insight of an expert combined with the playful enthusiasm of a connoisseur, The Art of Statistics is the definitive guide to the power of data.
"A call to arms for more social data literacy ... a reminder that there are passionate and confident statisticians who can eloquently argue that their discipline is needed now more than ever." - Financial Times
David Spiegelhalter is a British statistician and chair of the Winton Centre for Risk and Evidence Communication in the Statistical Laboratory at the University of Cambridge. In 2014 he was knighted for his services to statistics, and from 2017 to 2018 he served as president of the Royal Statistical Society. He lives in the United Kingdom.
"An important and comprehensive new book" ―Hannah Fry, The New Yorker
“David Spiegelhalter's Art of Statistics shows how we can use the ever-increasing flow of data to improve our understanding of the world… The art of statistics will serve students well. And it will be a boon for journalists. keen to use statistics responsibly - as well as anyone who wishes to approach research and its reports with healthy skepticism. "- Evelyn Lamb, Nature
“A book that contains so much statistical information and remains clear and readable is highly unlikely, and yet it is there. In an age of science bait, big data and personalized medicine, this would be a book almost anyone would benefit from reading, "" Stuart Ritchie, The Spectator
"What David Spiegelhalter is doing here is a very in-depth introduction to statistics without using mathematical formulas. And it's remarkable. Spiegelhalter is warm and encouraging - it's really fun to read ... This book should be required reading for everybody." Politicians, journalists, medical professionals, and anyone who tries to influence (or be influenced) people by statistics. A tour de force. "- Popular Science
"A call to arms for greater knowledge of social data ... Spiegelhalter's work is a reminder that there are passionate and confident statisticians who can eloquently say that their discipline is needed more than ever." (Financial Times)
Data and statistics are essential topics in many disciplines, including economics and psychology, and are viewed with very little interest by those who do not deal with them. It has a lot to do with the fact that statistics are just numbers.
But here's a book that made statistics a fun topic. It arouses curiosity and educates us painlessly. On the contrary, the book is written with exemplary clarity. Who are the people that Harold Shipman, the serial killer, killed? Using statistics, the author answers questions such as: Could Shipman have been captured earlier?
Does Eating Bacon Increase Our Risk For Cancer? How do we determine the accuracy of statistical studies? Are there any tips to keep in mind when considering the oft-cited axiom - “There are lies, bloody lies and statistics”? This book has all the answers, and you can enjoy reading it to finally understand a dark art.
This book can only be seen as a new statistical text aimed at improving the statistical literacy of the general public and conveying it with interesting, real-life, often high-profile, engaging and accessible examples. It works at that level, but the book is much more than that. "The Art of Statistics" is the compilation and distillation of David Spiegelhalter's 40 years of professional experience and wisdom, particularly in medical statistics, for which he was knighted in 2014.
It is important for Spiegelhalter to convey information in a clear and understandable manner. In particular, it explains the issues of risk and uncertainty in order to distinguish worrying fears from unfounded fears. Speigelhalter is chairman of the Winton Center for Risk and Evidence Communication at Cambridge University, whose motto is “Inform, not persuade” - those four words perfectly sum up this highly recommended book.
This is possibly the best single-volume survey available for statistical analysis and problem solving. Written vividly for a wide audience with plenty of case study examples of how interesting questions can be answered using statistics. The book also includes a glossary that delves into the algebra of statistical analysis. I highly recommend this book to beginners who want to know what to do with statistics, as well as to old-timers who appreciate this well-done roundup of the field.
Quite pleasant and not so dry to read for a statistics book. Almost easy to understand, but not that simple ...
Some points require further elaboration, for example in chapter 10, page 352: "The fact that this 95% interval contains 0 is logically equivalent to the point estimate (-3000) being less than 2 standard errors of 0, which means the change is not significantly different from 0."
- I'm totally lost here, I didn't understand what the author is trying to say. If there is any material in the previous chapter that might aid in understanding this sentence, the author should do some sort of review before diving into it. Otherwise further elaboration is required.
"A two-sided p-value is less than 0.05 if the 95% confidence interval does not contain the null hypothesis (usually 0)" - Seems to be a lot of information, but at the same time abstract in statistical terms. Completely lost. can explain this "p-value" somewhat more simply in the context of the terminology combination "two-sided", "95% confidence interval", "null hypothesis". I'm not a complete layman at statistics, but these terms and definitions still confuse me, especially now that they come together
The author, a skilled statistician, has done a remarkable job on this book, conveying the concepts and intricacies of statistics and statistical reasoning. In clear, user-friendly, authoritative, and accessible prose, it guides the reader through statistical analysis techniques, including the various pitfalls and abuses that can easily plague more than one. A glossary at the end of the book contains many definitions of many of the terms used in the book, as well as mathematical formulas for certain quantities for those interested.
This book could be of immense value as a reference for a formal statistics course at university. But interested math / statistics enthusiasts looking for friendly, non-math explanations for often very counter-intuitive concepts can also enjoy it.
This book, which is certainly not suitable as a text for beginner statistics, is a very clever picture, very well coordinated and presented to help us step back and understand beginner level statistics. I think much of the book can be recommended as useful to mathematicians who (like me) sometimes teach beginner statistics in community college or high school. The author puts discussions about many small technical things back into a glossary that is useful for teachers who are also mathematicians and understand a large part of the "traditional" statistics that are taught at the beginner level.
Reading the notes at the end of the book can also be helpful. I don't know how much his book would make sense to a student who is new to statistics. Some of his more pertinent comments, philosophical points, and discussions need to be put in a meaningful context, and I think that would be quite difficult for someone with little or no experience in statistics.
But I think his presentation, which uses the traditional "Neyman-Pearson-Fisher" approach as a framework and reference and very intelligently discusses many more modern views such as algorithms for deep learning, is a great approach, especially as it allows him to come up with important ideas to communicate by banning math in his glossary. While he is an advocate of Bayesian statistics, he does not discuss this approach until late in the book, which is a very good idea as we can see his criticism of the traditional "Neyman-Pearson-Fisher" course in a very clear way , largely without subtle bias, and also better appreciates what Bayesian approaches it represents. Overall, his treatment is extremely balanced, very revealing both through his keen observations and through his high level of expertise, which he imparts well, and also very much oriented towards the revolutions of our thinking and our methods that are favored by the computer and AI revolution .
Therefore, I highly recommend this book to mathematicians in high schools or colleges who teach statistics, and to some extent students who have some experience in statistics.
Many books have tried to explain the basic concepts of statistics with a minimum of math, but this book will certainly be the "gold standard" for the 2020s. It combines polished rendering with an impressive collection of interesting data examples. of the real world. In part, the usual basics of textbooks are covered - summary statistics, charts, randomized controlled experiments, sampling, regression, statistical significance, Bayes. Then modern ideas like algorithmic prediction (and one of my personal favorites, Brier scores). He puts a lot of emphasis on "when the going
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