In 2016, all the best money was on Hillary Clinton to defeat Donald Trump in the U.S. Presidential election. Massive polls analyzed by highly-credentialed experts said so. The election went the other way. How could the analysts have gotten it so wrong? A potential answer is found in the pages of this short book written more than sixty years earlier by Darrell Huff and charmingly (if whimsically) illustrated by Irving Geis.
This book was recommended to me in passing by one of my professors when I was completing the capstone course for my BS in mathematics. Largely because of who suggested it, I expected a book about mathematical statistics. Instead, this is a book about understanding how statistical analysis can be abused (by journalists, politicians, advertisers, etc., etc.). It does not denigrate the practice of statistical analysis itself (though you will not learn even a single technique from statistical theory in its 144 pages), but rather serves as a lighthearted cautionary tale about how easy it is to become convinced that statistics carry all the weight of science even though statistical analysis is both science and art.
The reader already well-versed in statistics will not find any new information but will still be pleased by the book's artful presentation of known ideas. Readers who are not so well-versed in statistics should consider this book required reading because it succinctly explains how the information we all consume every day may have been manipulated--intentionally or otherwise--to give us false impressions.
In fact, I would argue the value of this book has only increased in the decades since its initial publication. While the reader picking up this book upon its publication in 1954 would surely encounter plenty of statistics and graphs throughout the week, our modern 24-hour news cycle and constant immersion in a multimedia world has magnified the opportunity for statistical deception. Of course, you'll find that the book's examples are outdated (references to an exorbitant $25,000 salary for Yale graduates might seem at first more quaint than informative). However, despite the dated examples, the statistical phenomena described are as relevant as ever. Indeed, an argument could be made that the examples from yesteryear might even aid the book's pedagogical value by avoiding the contemporary issues that might cause the reader to don partisan intellectual blinders.
If I were to criticize, I would say that the book fails as an introduction to statistical thinking. For example, it rightly cautions the reader to beware of the difference between median and mean when interpreting reported "averages," but fails to provide much insight regarding when each of these measures of central tendency might be superior to the other. As such, the reader looking for insight regarding the practice of statistics, even from a non-technical perspective, may be disappointed. However, the reader interested in the consumption of statistical information will find a wealth of information packed into a charming little book.
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Reviews with images
AFTER YOU READ "HOW TO LIE WITH STATISTICS" YOU'LL NEVER LOOK AT POLLS, SURVEYS & ADS IN THE SAME WAY!
This author taught me something I should already know. I knew it happened but didn't realize how many areas of our lives are manipulated by numbers. Having a background in media, I learned how some statistics can be, shall I say, modified to make the point a Radio/TV Station wants to make. I'm a hard sell on stuff like this. However, Darrell Huff cleverly outs the "clever" statisticians who use everything from sample studies, interview techniques, tabulations & every method conceivable to have us believe what they want us to. "How to Lie With Statistics",is an informative wake up, an easy read, with illustrations by Irving Geis, that lend this rather serious subject some whimsy! Entertains, informs & kind of shakes your faith in the good old, "87% of people studied (some small print) prefer So & So's deep fried Raisins for improved memory function". After finishing Huff''s short, but sweet 144 page heads up on how numbers lie, you won't ever think the same when reading a statistic!
Top reviews from other countries
Adam Glinglin
a must read for statistical numeracy
Reviewed in Canada on January 14, 2024
JTN
A great summary of key things to remember when reviewing data
Reviewed in the United Kingdom on February 2, 2024Amazon Kunde
Old, but still relevant. Not a modern read.
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Sempre actual...
Reviewed in Spain on October 23, 2022
Luís Igreja
Reviewed in Spain on October 23, 2022
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