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How to Lie with Statistics Paperback – October 17, 1993

ISBN-13: 978-0393310726 ISBN-10: 0393310728 Edition: Reissue

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Product Details

  • Paperback: 144 pages
  • Publisher: W. W. Norton & Company; Reissue edition (October 17, 1993)
  • Language: English
  • ISBN-10: 0393310728
  • ISBN-13: 978-0393310726
  • Product Dimensions: 8.3 x 5.6 x 0.4 inches
  • Shipping Weight: 7.2 ounces (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (222 customer reviews)
  • Amazon Best Sellers Rank: #1,494 in Books (See Top 100 in Books)

Editorial Reviews

Amazon.com Review

"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.

Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!

Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.

Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton

Review

“Mr. Huff's lively, human-interest treatment of the dry-as-bones subject of statistics is a timely tonic.... This book needed to be written, and makes its points in an entertaining, highly readable manner.” (Management Review)

“Illustrator and author pool their considerable talents to provide light lively reading and cartoon far which will entertain, really inform, and take the wind out of many an overblown statistical sail.” (Library Journal)

“A pleasantly subversive little book, guaranteed to undermine your faith in the almighty statistic.” (Atlantic)

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Customer Reviews

The book is about how statistics can be used to lie and mislead.
M. Broderick
It explains statistics in clear, easy-to-understand language and has very entertaining cartoons to show examples.
R. Mathews
I read this book on the day before I started teaching a new class in basic statistics.
Charles Ashbacher

Most Helpful Customer Reviews

95 of 96 people found the following review helpful By Michael R. Chernick on February 12, 2008
Format: Paperback
Statisticians hate the old adage "Lies, Damned Lies and Statistics", but statistical methods do have that reputation with the general public. There are many excellent accounts, some even understandable to laymen that explain the proper ways to analyze, study and report the analysis of statistical data. Huff's famous account is illustrative and well written. It gives the average guy a look at how statistics is commonly misused (either unintentionally or deliberately) in the popular media. Graphical abuses are particularly instructive. Readers should recognize that statistical methods are scientific and with proper education anyone should be able to recognize the good statisticians from the charletons. For now Huff's book is still a good starting place. As a statistician I hate the public image portrayed in the quote above. However, I do sometimes have fun with it myself. As I write this review I am in my office wearing a sweatshirt that reads "When all else fails manipulate the data."

A modern book by a consulting statistician on the same topic is "Common Errors in Statistics and How to Avoid Them" by Phil Good. If you enjoy this book take a look at Good's book also.
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115 of 119 people found the following review helpful By Mary P. Campbell on July 3, 2001
Format: Paperback
This book, written in 1954, is just as pertinent today (perhaps even more so, as it's so easy to acquire statistics due to our current technology) -- Darrell Huff gives people the tools to talk back to statistics. Though there is a little bit about deliberate deception, in such things as "The Gee-Whiz Graph" (about how the graphical display of statistics can be twisted so that one can get any desired result, though the stats aren't changed), the meat of the book is regarding sound statistical reasoning, something that people today really need to consider.
For example, every person who listens to the latest survey showing a correlation between certain food and certain health problems or benefits should read "Post Hoc Rides Again", in which people erroneously leap from statistical correlation to a cause-and-effect relationship. An example given in the book is a report in which it was found that smokers had lower grades in college; ergo, said the researcher, smokers wishing to improve their grades should quit smoking! Of course, a statistical study showing that there's a "significant" relation between smoking and low grades doesn't show which causes the other -- perhaps educational failure draws people to smoke! My own theory would be that the =type= of person who is given to smoking is also given to not doing well in school; instead of cause and effect, one has a correlation from a shared, third (and unnamed) cause. One comes across these fallacies in the news =every=day=; I've been reading my online news, and in the science section I've already found two suspicious cause-and-effect reports. As Huff notes, it's not the statistics which are in question -- it's how they're used.
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62 of 65 people found the following review helpful By John Nolley II VINE VOICE on January 9, 2004
Format: Paperback
Although "How to Lie with Statistics" is a bit dated (having been written in the 1950's), the principles it puts forth are still valid today--if not moreso than ever--and the material is delivered in clear, concise, and even entertaining anecdotes and illustrations.
How often do you hear statistics bandied about in the media or used to try to prove some special-interest point? "Of course" the people quoting the figures must be right with numbers on their sides... until you look at just how those numbers were arrived at.
This book isn't truly a guide on how to lie with statistics, but it is an excellent text that informs the reader both how others will lie to them using statistics and on how to interpret the validity of purported statistical data.
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30 of 30 people found the following review helpful By wiredweird HALL OF FAMETOP 500 REVIEWER on January 12, 2005
Format: Paperback Verified Purchase
-- with no equations. This book really is for every one. In fact, if you're a no-equations reader, this book will be especially helpful.

It shows all the little tricks that advertisers and propagandists, government agencies included, throw at you every day. One, p.85, is an impressive sounding news article about teachers' pay. At first, it looks as if a generous government outlay had doubled or tripled teachers' salaries. Looking closer, however, one sees an odd cluster of unrelated numbers flying in close formation. None of the numbers quoted has any bearing on any other, at least none that the article's reader can discover.

Duff also points out the fallacy of correlation. Oh, it's a useful enough measure, if (!) a number of mathematical requirements are met. It is not causation, however. For example, there is a strong correlation between a school child's height and the child's score on a given spelling test - taller kids do better. The fact is a lot less surprising when you see that first graders tend to be smaller than sixth graders, and tend to know fewer words. Maybe the example sounds silly, but no sillier than lots of the numbers in the news every day.

This is a quick and approachable read, and true even if the examples are now dated. Despite its name, this book really is aimed at honest people, readers who want real understanding of the data thrown at them, and presenters who want their numbers to be understood properly. And best, you don't have to be a mathematician to see what's going on.

//wiredweird
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