How to Lie With Statistics [Paperback]

Darrell Huff (Author), Irving Geis (Author)

Book Description

Publication Date: January 19, 1954 | ISBN-10: 039309426X | ISBN-13: 978-0393094268 | Edition: 1

Darrell Huff runs the gamut of every popularly used type of statistic,probes such things as the sample study, the tabulation method, theinterview technique, or the way results are derived from the figures,and points up the countless number of dodges which are used to foolrather than to inform.

--This text refers to the Kindle Edition edition.

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 --This text refers to an alternate Paperback edition.

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.” --This text refers to the Kindle Edition edition.

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Product Details

• Paperback: 144 pages
• Publisher: W. W. Norton; 1 edition (January 19, 1954)
• Language: English
• ISBN-10: 039309426X
• ISBN-13: 978-0393094268
• Product Dimensions: 8.4 x 5.5 x 0.6 inches
• Shipping Weight: 7.2 ounces (View shipping rates and policies)
• Average Customer Review: 4.6 out of 5 stars  See all reviews (129 customer reviews)
• Amazon Best Sellers Rank: #908,624 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

84 of 86 people found the following review helpful:

4.0 out of 5 stars Statistics don't lie; people do., July 3, 2001

By 

Mary P. Campbell "math geek" (Flushing, NY USA) - See all my reviews
(REAL NAME)   

This review is from: How to Lie with Statistics (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.

Some of the figures and examples used are funny due to their datedness (I love the picture of the surveyor asking a doctor what brand of cigarette he smokes, and the cigar-smoking baby just makes me smirk). It seems to me if you multiply every monetary amount by 10, you might get a better idea as to what it's worth (I don't know what it is actually worth, as I don't know what the inflation from 1954 is (another suspicious statistic)).

More to the point, with the help of this book, you need not have blind faith in the numbers or disgustedly throw all stats away. The mathematics of statistics guarantees them to have great power, as long as you know how to interpret them correctly. You might be pleasantly surprised to find that more common sense than math is involved in this book, but the truth is most modern abuse of numbers happens well after the numbers have been calculated. Of course, once you talk back to statistics people may think you're crazy; at least you won't be fleeced by false reasoning.

52 of 52 people found the following review helpful:

5.0 out of 5 stars very popular account of how statistics can be misused, February 12, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: How to Lie with Statistics (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.

47 of 48 people found the following review helpful:

5.0 out of 5 stars An Entertaining Primer on the Validity of Statistics, January 9, 2004

By 

John Nolley II (Fairfax, VA United States) - See all my reviews
(VINE VOICE)   

This review is from: How to Lie with Statistics (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.