Content Warning: The source material and this guide include discussions of suicide and systemic racism.

Chapter 1 covers the problem of sample bias in statistical data. Darrell Huff explains that it isn’t possible to count every individual when studying a population, so a statistician needs to create a sample representing the whole. To be the most accurate, a representative sample needs to be “one from which every source of bias has been removed” (20). Sample bias, he notes, appears in many different forms.

Huff introduces the chapter’s issue with an example: the reported amount of money made by an average graduate from a given year. The problem with the statistic, Huff says, comes from biased sampling. All the graduates were probably not contacted, and those who responded were likely more influential and made more money than those who chose not to. That creates a bias in the sample toward one part of the population over the other and yields an inaccurate result.

The following section covers the problem that the reported numbers could be made up, even with a decent sample. Huff’s examples here highlight that people often lie to make themselves look better in polls, from how much money they make to the media they consume or even their health habits.