How to Lie with Statistics

Chapter 2 covers the problem of averages in statistics and how they can be used to deceive the reader. Huff says that reporting an average can vary without lying or giving false information. Instead, the type of average used for a statistic can mislead the audience toward the wrong conclusions. The three main types of averages used in statistics are the mean, the median, and the mode.

Huff begins by giving a fictional example in which he changes the average income for a neighborhood depending on which average he uses. While he notes that sometimes the averages are the same or close enough not to be a problem, this is often false. In this case, the word “average” is meaningless unless the reader knows what kind it is. Huff describes this issue with a distribution curve. If the averages are close, the curve has a bell shape. However, if the numbers have a wide variation, the distribution curve becomes asymmetrical, which Huff describes as shaped like a slide. He gives an example of the average wage at a company, breaking down the process of arriving at the different averages and why some look “better” than others. The “averages” here change because most people in the population have low incomes, while a few high earners skew the distribution. He says regarding the high-earning outlier, “You have in reality the case that sounds like a joke or a figure of speech: Nearly everybody is below average” (33). The statistic can fool the audience if the average does not reflect most of the target population, such as a company’s workers.

Huff concludes that his previous examples on averages are simplified. However, they serve to familiarize the reader with the problem. He says the most critical question is what the average is and who is included.