How to Lie with Statistics

Chapter 7 covers the issue of the “semiattached” figure. Huff describes this in statistics as a situation in which one issue is demonstrated, but the results are used to prove another semi-related—or completely unrelated—thing.

Huff begins the chapter with a simple example of accomplishing the semiattached figure. A test is done in a laboratory that gives accurate and demonstrable results. When publishing the data, however, the person doing so links it to health benefits that cannot be proven. Huff continues by providing more examples, such as that of a hypothetical poll made to disprove racial prejudice. The poll actually measured the prejudice but could be spun to present the desired results. He follows with examples from advertising, including advertising cigarette brands by how many doctors smoke them or an electric juicer that used an irrelevant comparison to make its claims.

The following section continues the thread by examining the statistics of accidents. Here, Huff focuses on the issue of proportions when looking at statistical results. For example, he notes that more accidents are reported in certain types of weather or times of day, but this result is not because they are more dangerous; instead, more people are traveling under those circumstances than otherwise. Using this higher base number without considering the proportion is another example of semiattachment.

Huff continues by talking about other ways of creating semiattached figures. The main one, he says, is done by connecting “two things that sound the same but are not” (82). Minor complaints are used as evidence of total opposition, for example. The trick can also describe numbers, such as earnings, that appear too high.

The following section concerns unintentional examples of semiattachment. Huff says that issues with the source can create statistical results that don’t match reality. One location might underreport an illness, for example, or a comparison of groups might fail to consider outside variables. Huff compares this to “before” and “after” photographs that show a transformation in a person or a place when other changes, such as lighting or other visual tricks, created the desired results, rather than the advertised factor.