How to Lie with Statistics

discarding unfavorable data

Correlation-or-Causation

How to Lie with Statistics‘ is a book written by Darrell Huff in 1954 presenting an introduction to statistics for the general reader. Huff was a journalist who wrote many ‘how to’ articles as a freelancer, but was not a statistician. The book is a brief, breezy, illustrated volume outlining errors when it comes to the interpretation of statistics, and how these errors lead to incorrect conclusions. In the 1960s and ’70s it became a standard textbook introduction to the subject of statistics for many college students. It has become one of the best-selling statistics books in history, with over one and a half million copies sold in the English-language edition, and has also been widely translated.

Themes of the book include ‘Correlation does not imply causation’ and ‘Using random sampling.’ It also shows how statistical graphs can be used to distort reality, for example by truncating the bottom of a line or bar chart, so that differences seem larger than they are, or by representing one-dimensional quantities on a pictogram by two- or three-dimensional objects to compare their sizes, so that the reader forgets that the images do not scale the same way the quantities do. The original edition contained humorous illustrations by artist Irving Geis. In a UK edition these were replaced with cartoons by Mel Calman.

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One Comment to “How to Lie with Statistics”

  1. Distortion by re-framing or implicit false premise syllogism (correlation = causation) is true in a lot of fields. It’s perhaps easiest in statistics because of the implication that numeric data is in some way incontrivertible. But it happens in other fields too. I see it reasonably often in history.

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