Naked Statistics warns about various ways that statistics can either create the illusion of certainty or be maliciously applied in the service of ulterior motives. 

One trap Wheelan dwells on is the appeal of precision, showing how a number that sounds exact can still mislead when the data, assumptions, or context collapse under scrutiny. He focuses on how people often trust tidy statistics without question, which can hide complexity, support poor decisions, or feed larger failures. Wheelan ties statistical integrity to a habit of examining a number’s source and meaning, with broad accuracy taking priority over exactness that misrepresents reality. For example, a golf range finder once gave him exact distances, but since it was set to measure in meters and not yards, its precise readings entirely useless. Wheelan then moves to examples with larger stakes. In 1950, Senator Joseph McCarthy said he had a list of “205” communists in the State Department (37), and the specificity of the number gave the claim an air of credibility even though it was a lie. Wheelan uses these moments to show how precise figures hide shaky foundations and gain authority they have not earned. 

Even more dramatically, Wheelan uses Wall Street’s Value at Risk models before the 2008 crisis to show how a single dollar figure summarized maximum potential loss. The number created a sense of safety because it looked exact, even though the models relied on assumptions drawn from a calm period in the market and ignored rare, catastrophic events known as tail risk. That misplaced trust in a tidy figure played a part in the 2008 collapse. For Wheelan, the VaR story illustrates how a neat calculation can distract from the real shape of danger.

Wheelan also shows how real data becomes misleading when people present it with too little context. He uses the debate over American manufacturing to illustrate this point. Rising output paints a seemingly bright picture, while falling employment points toward the reality of decline experienced by workers who have lost their jobs. The misuse of statistics in this case highlights a number that tells an incomplete story, hiding the fact that productivity gains mean that factories need fewer workers to make more goods. Wheelan makes a similar point about an advertising campaign war between AT&T and Verizon, which used different units of analysis to make their levels of wireless coverage seem better than those of their competitors. AT&T counts the number of Americans that live within its network, which mostly covers the most populated places in the country, while Verizon counts the area of the US that its service reaches. Each company thus picks the framing that flatters its network, concealing context that would help consumers make informed decisions.