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"Revised and updated edition of a standard in the field. Alerts readers to the problems, inherent in statistical practice-illustrating the types of misused statistics with well-documented, real-world examples, nearly half new to this edition, drawn from a wide range of areas, including the media, public policy, polls and surveys, political elections and debates, advertising, science and health care, and business and economics."
Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.
This volume focuses on the abuse of statistical inference in scientific and statistical literature, as well as in a variety of other sources, presenting examples of misused statistics to show that many scientists and statisticians are unaware of, or unwilling to challenge the chaotic state of statistical practices.;The book: provides examples of ubiquitous statistical tests taken from the biomedical and behavioural sciences, economics and the statistical literature; discusses conflicting views of randomization, emphasizing certain aspects of induction and epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as instant formulas to draw causal relationships; treats constructive uses of statistics, such as a modern version of Fisher's puzzle, Bayesian analysis, Shewhart control chart, descriptive statistics, chi-square test, nonlinear modeling, spectral estimation and Markov processes in quality control.
If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.
A New York Times bestseller "Brilliant, funny…the best math teacher you never had." —San Francisco Chronicle Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions. And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
In this sequel to the acclaimed Damned Lies and Statistics, which the Boston Globe said "deserves a place next to the dictionary on every school, media, and home-office desk," Joel Best continues his straightforward, lively, and humorous account of how statistics are produced, used, and misused by everyone from researchers to journalists. Underlining the importance of critical thinking in all matters numerical, Best illustrates his points with examples of good and bad statistics about such contemporary concerns as school shootings, fatal hospital errors, bullying, teen suicides, deaths at the World Trade Center, college ratings, the risks of divorce, racial profiling, and fatalities caused by falling coconuts. More Damned Lies and Statistics encourages all of us to think in a more sophisticated and skeptical manner about how statistics are used to promote causes, create fear, and advance particular points of view. Best identifies different sorts of numbers that shape how we think about public issues: missing numbers are relevant but overlooked; confusing numbers bewilder when they should inform; scary numbers play to our fears about the present and the future; authoritative numbers demand respect they don’t deserve; magical numbers promise unrealistic, simple solutions to complex problems; and contentious numbers become the focus of data duels and stat wars. The author's use of pertinent, socially important examples documents the life-altering consequences of understanding or misunderstanding statistical information. He demystifies statistical measures by explaining in straightforward prose how decisions are made about what to count and what not to count, what assumptions get made, and which figures are brought to our attention. Best identifies different sorts of numbers that shape how we think about public issues. Entertaining, enlightening, and very timely, this book offers a basis for critical thinking about the numbers we encounter and a reminder that when it comes to the news, people count—in more ways than one.
In this edition, efforts have been made to assist readers in converting data into useful information that can be used by decision-makers in making more thoughtful, information-based decisions.
Mathematics scares and depresses most of us, but politicians, journalists and everyone in power use numbers all the time to bamboozle us. Most maths is really simple - as easy as 2+2 in fact. Better still it can be understood without any jargon, any formulas - and in fact not even many numbers. Most of it is commonsense, and by using a few really simple principles one can quickly see when maths, statistics and numbers are being abused to play tricks - or create policies - which can waste millions of pounds. It is liberating to understand when numbers are telling the truth or being used to lie, whether it is health scares, the costs of government policies, the supposed risks of certain activities or the real burden of taxes.