# How to Lie With Statistics

## by Darrell Huff, illustrated by Irving Geis

### July 2001

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.

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Mary Pat Campbell, last updated July 2001