How to Lie with Statistics

Chapter 4 focuses on creating impressions of a significant difference when the reality is small to nonexistent. Huff begins the chapter by discussing error in statistics and how its omission from reporting can lead to inaccuracies. He starts with the example of two people taking an IQ test. He takes issue with using the results as a measure of actual intelligence because the test considers only a few factors as indicating “intelligence,” but he focuses on the importance of the concept of error in interpreting the results. Huff explains how probable error works, the type of error he focuses on in the chapter over standard error. Because IQ test results have a range of statistical error, the results of students with an IQ of 98 and an IQ of 101 overlap. Thus, one should not be regarded as more intelligent than the other based on these numbers, because this difference places the students within the same statistical range based on the test’s “probable error” of +/- 3%. When looking at statistics with error, the reader should “keep that plus-or-minus in mind, even (or especially) when it is not stated” (59). If the probable or statistical error is ignored, the importance of the result becomes inflated.