Over Half a Million Copies Sold--an Honest-to-Goodness Bestseller
Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to full rather than to inform.
SAMPLE CUSTOMER REVIEWS –
1) very popular account of how statistics can be misused - 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.
By Michael R. Chernick on February 12, 2008
2) Statistics don't lie; people do. - 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.
By Mary P. Campbell on July 3, 2001
Click –How to Lie with Statistics
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.
By Mary P. Campbell on July 3, 2001
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