How to Lie with Statistics

“The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify.”

Darrell Huff explains how manipulative statistics can mislead so easily. American culture at the time of the book’s publication emphasized hard facts over opinion. Therefore, using statistical data to make a point was put on a pedestal. Those seeking to manipulate can use statistics as a justification to exaggerate their agendas.

“If your sample is large enough and selected properly, it will represent the whole well enough for most purposes. If it is not, it may be far less accurate than an intelligent guess and have nothing to recommend it but a spurious air of scientific precision.”

Huff uses this quote to discuss statistical samples; it is part of his more extensive discussion on correct sampling. He criticizes using bad sampling or sampling that is too small for statistical analysis. Bias in the sample often skews the results far enough from reality that it is useless. The only thing it has for support is the veneer of respectability gained from being “scientific.” As he says, the reader is better off guessing than trusting the results of a bad sample.

“It is equally true that the result of a sampling study is no better than the sample it is based on. By the time the data have been filtered through layers of statistical manipulation and reduced to a decimal-pointed average, the result begins to take on an aura of conviction that a closer look at the sampling would deny.”

Huff again talks in this quote about issues with faulty sampling practices. The statistician can attempt to make the results appear more legitimate, but this legitimacy is surface-level. If the sample has too many problems, the statistic is flawed from the start, and no amount of dressing it up can change that.