How to Lie with Statistics

by Darrell Huff

semiattachment: It assumed that newspaper space given to crime reporting is a measure of crime rate.

The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify.

The crooks already know these tricks; honest men must learn them in self-defense.

It is equally true that the result of a sampling study is no better than the sample it is based on. By the time the data have been filtered through layers of statistical manipulation and reduced to a decimal-pointed average, the result begins to take on an aura of conviction that a closer look at the sampling would deny.

The test of the random sample is this: Does every name or thing in the whole group have an equal chance to be in the sample? The purely random sample is the only kind that can be examined with entire confidence by means of statistical theory, but there is one thing wrong with it. It is so difficult and expensive to obtain for many uses that sheer cost eliminates it. A more economical substitute, which is almost universally used in such fields as opinion polling and market research, is called stratified random sampling. To get this stratified sample you divide your universe into several groups in proportion to their known prevalence.

Polls, they point out, seem to elect Republicans even when voters shortly thereafter do otherwise.

Sooner or later, by the operation of chance, a test group is going to show a big improvement worthy of a headline and perhaps a whole advertising campaign.

Only when there is a substantial number of trials involved is the law of averages a useful description or prediction.

Public pressure and hasty journalism often launch a treatment that is unproved, particularly when the demand is great and the statistical background hazy.