The Plausibility Check

Definition

Before analyzing a numeric claim in detail, ask whether it could even be true, using rough mental arithmetic and everyday knowledge rather than precision. Many false statistics collapse the moment you multiply out their implications or compare them against a known reference point. As Levitin puts it, when checking plausibility "we don't care about the exact numbers" — only whether the claim survives contact with common sense.

In the Book

Levitin opens the book's first chapter with this technique and drills it through several concrete examples. A claim that marijuana smokers in California doubled every year for thirty-five years is shown to be impossible by simply doubling a starting number of one repeatedly — it yields over 17 billion people, more than the world's population, using nothing but elementary-school arithmetic. A telemarketing boss's claim of "1,000 sales a day" fails once you time out how long a single call realistically takes (dialing, ringing, pitching, and taking payment), capping even a frantic best case near 480. A widely circulated claim that "150,000 girls and young women die of anorexia each year" is refuted by comparing it to CDC total-death figures for that age group, which are far lower than the anorexia claim alone. Not every claim resolves this cleanly — Levitin also shows a headline like "more people have cell phones than toilets" passing the plausibility test even though it still needs further verification, distinguishing "not ridiculous" from "confirmed."

Why It Matters

The plausibility check is a cheap, general-purpose filter that catches a large share of false or fabricated numbers before you spend real effort investigating them, and it requires no specialized statistical training — just the willingness to do the arithmetic and compare the result to something you already know. It reframes numeracy as a habit of estimation rather than a credential, and it generalizes to any domain where claims arrive faster than anyone can verify them: budgets, headlines, sales pitches, scientific press releases. It draws a useful line between "implausible, reject" and "plausible but unconfirmed, investigate further."