Chapter 3 covers the small things that often go missing from published statistics. Huff notes that the problem with missing information is that it usually needs to be pointed out to general audiences. Because members of the public don’t know they should look for these details, those who make deceitful statistics can present wild claims without being caught.

Huff begins by returning to the problem of samples by talking about those that are too small. In statistics, the use of large sample sizes produces the best results. Misleading statistics sometimes favor small sample sizes. The reason for this, Huff says, is that “with a large group any difference produced by chance is likely to be a small one and unworthy of big type” (40-41). An insignificant difference in a population appears more significant in a small sample than in a large one, which makes it look more meaningful and dramatic for advertising purposes, for example. He uses flipping a coin to make his point: In his small sample of flips, one side came up more often than another, but if enough flips are done, the probability of half heads and half tails emerges.

Huff continues by talking about the issue of how big is big enough for a sample size.