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How to Lie with Statistics Paperback – 12 Dec. 1991
| Darrell Huff (Author) See search results for this author |
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In 1954, Darrell Huff decided enough was enough. Fed up with politicians, advertisers and journalists using statistics to sensationalise, inflate, confuse, oversimplify and - on occasion - downright lie, he decided to shed light on their ill-informed and sneaky ways. How to Lie with Statistics is the result - the definitive and hilarious primer in the ways statistics are used to deceive.
With over one and half million copies sold around the world, it has delighted generations of readers with its cheeky takes on the ins and outs of samples, averages, errors, graphs and indexes. And in the modern world of big data and misinformation, Huff remains the perfect guide through the maze of facts and figures that are designed to make us believe anything.
- Print length128 pages
- LanguageEnglish
- PublisherPenguin
- Publication date12 Dec. 1991
- Dimensions1.02 x 12.7 x 19.3 cm
- ISBN-100140136290
- ISBN-13978-0140136296
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From the Back Cover
With over one and half million copies sold around the world, it has delighted generations of readers with its cheeky
About the Author
Product details
- Publisher : Penguin; 1st edition (12 Dec. 1991)
- Language : English
- Paperback : 128 pages
- ISBN-10 : 0140136290
- ISBN-13 : 978-0140136296
- Dimensions : 1.02 x 12.7 x 19.3 cm
- Best Sellers Rank: 13,488 in Books (See Top 100 in Books)
- Customer reviews:
About the authors

Darrell Huff (July 15, 1913 – June 27, 2001) was an American writer, and is best known as the author of How to Lie with Statistics (1954), the best-selling statistics book of the second half of the twentieth century.
Huff was born in Gowrie, Iowa, and educated at the University of Iowa, (BA 1938, MA 1939). Before turning to full-time writing in 1946, Huff served as editor of Better Homes and Gardens and Liberty magazine. As a freelancer, Huff produced hundreds of "How to" feature articles and wrote at least sixteen books, most of which concerned household projects. One of his biggest projects was a prize-winning home in Carmel-by-the-Sea, California, where he lived until his death.
Stanford historian Robert N. Proctor wrote that Huff "was paid to testify before Congress in the 1950s and then again in the 1960s, with the assigned task of ridiculing any notion of a cigarette-disease link. On March 22, 1965, Huff testified at hearings on cigarette labeling and advertising, accusing the recent Surgeon General's report of myriad failures and 'fallacies'."
First and foremost, though, Huff is credited with introducing statistics to a generation of college and high-school students on a level that was meaningful, available, and practical, while still managing to teach complex mathematical concepts. His most famous text, How to Lie with Statistics, is still being translated into new languages. His books have been published in over 22 languages, and continue to be used in classrooms the world over.
Bio from Wikipedia, the free encyclopedia.

Irving Geis (October 18, 1908 – July 22, 1997) was an American artist who worked closely with biologists. Geis's hand-drawn work depicts many structures of biological macromolecules, such as DNA and proteins, including the first crystal structure of sperm whale myoglobin.
Bio from Wikipedia, the free encyclopedia.
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If you spotted the fast one I pulled in the first paragraph, you're either one of "the crooks" who already know these tricks or else are an honest soul who has learned them "in self-defence". Hence the title of this fantastic little book: knowing how a burglar thinks helps secure your house. Most of the time, I would pass over the phrase "average wage" without a second glance. We all know what an average is, don't we? Distant maths lessons are just that for most of us, and even if I'd dredged up the question - what kind of average? - would I have been bothered to ask it? Complacency translates into vulnerability.
"When you are told that something is an average you still don't know very much about it unless you can find out which of the common kinds of average it is - mean, median, or mode." Without a clear understanding of these different kinds of average, you have to hope it doesn't really matter which one is being used, but this is only the case "when you deal with data... that have the grace to fall close to what is called the normal distribution." Otherwise, it makes a big difference, so much so that, "as usually is true with income figures, an unqualified 'average' is virtually meaningless."
Advertisers, of course, are among the most culpable and capable when it comes to lying with statistics (although at least their motives are plain). It is typical of Huff's sense of mischief that, alongside the calculations, he presents us with an ethical dilemma of enormous proportions: should we feel sorry for advertisers who are themselves victims of statistical skulduggery? For example, a magazine publisher is happy to state the median age of its readership, while leaving the kind of average for incomes "carefully unspecified". "Could it be that the mean was used instead because it is bigger, thus seeming to dangle a richer readership before advertisers?"
This is a short book, made even shorter by pictures of cows and charts that take up half a page. (How the innocent-looking graph can be manipulated by adding "schmaltz" is another example of Huff's style: a simple unpicking of the familiar to demonstrate an important point.) It is also unreasonably funny in parts. I don't recall maths, let alone statistics, ever being this entertaining at school. And yet the intellectual content is not compromised. Huff's message is a serious one and perhaps more important now, since our propensity for attaching numbers to almost anything shows no sign of diminishing. It ought to be common knowledge that samples can be "biased by the method of selection", that "well-biased samples can be employed to produce almost any result anyone may wish", that it can be difficult to obtain "a representative sample... one from which every source of bias has been removed", that people who answer survey questions have "a desire to give a pleasing answer", that strange results "crop up when figures are based on what people say".
Most of us can understand these ideas when they are explained by someone like Huff (although it might help not to be an aristocrat). If we're honest, out in the wild without a guide, we're not so sure. Have you ever been scared by "accident statistics"? Would the fact that more people "were killed by aeroplanes last year than in 1910" give you pause for thought? Are modern planes really more dangerous? "Nonsense. There are hundreds of times more people flying now, that's all."
"It is sometimes a substantial service simply to point out that a subject in controversy is not as open-and-shut as it has been made to seem." Or, as Goldacre's slogan has it: "I think you'll find it's a bit more complicated than that..." Both belong to that noble tradition of satire with a serious message, and it is a tribute to Huff's writing style that he can end with a quote from Mark Twain that fits perfectly: "There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact."
The basic concept is that everyone, including customers, are accountable for their actions and therefore their impact on the success of the company. It highlights an important and fundamental problem associated with bottom-line focussed companies not acknowledging or realizing that not only are customers important to their profit but so are the workforce.
Thiss book addresses this in a well-structured way and is transparent in describing the journey of his company to a point where the profit has increased and staff are engaged with the company as stack holders. An excellent read and well worth giving as a gift, as the methods can be applied to other situation like parenting, relationships to name two.
Stimulated by the ever-increasing use of statistics by politicians, commercial organisations , environmentalists and others, I am well on the way to completing a book on this subject myself. Still, we see such techniques as comparing two percentages (without any detail of the actual numbers involved - 80% of 20 is hardly to be compared with 60% of 20 000), references to the results of surveys -with no indication of how many people were surveyed, how the sample was selected, how the questions were asked and so on.








