How to Lie with Statistics

One of the trickiest ways to misrepresent statistical data is by means of a map.

Permitting statistical treatment and the hypnotic presence of numbers and decimal points to befog causal relationships is little better than superstition.

In our time it is easy to show a positive correlation between any pair of things like these: number of students in college, number of inmates in mental institutions, consumption of cigarettes, incidence of heart disease, use of X-ray machines, production of false teeth, salaries of California school teachers, profits of Nevada gambling halls.

Given a small sample, you are likely to find some substantial correlation between any pair of characteristics or events that you can think of.

The fallacy is an ancient one which, however, has a powerful tendency to crop up in statistical material, where it is disguised by a welter of impressive figures. It is the one that says that if B follows A, then A has caused B.

You can, for instance, express exactly the same fact by calling it a one per cent return on sales, a fifteen per cent return on investment, a ten-million-dollar profit, an increase in profits of forty per cent (compared with 1935- 39 average), or a decrease of sixty per cent from last year. The method is to choose the one that sounds best for the purpose at hand and trust that few who read it will recognize how imperfectly it reflects the situation.

is not the same kettle of fish

There are many other forms of counting up something and then reporting it as something else. The general method is to pick two things that sound the same but are not.

The habitat of the bar chart is the geography book, the corporation statement, and the news magazine. This is true also of its eye-appealing offspring.

How accurately your sample can be taken to represent the whole field is a measure that can be represented in figures: the probable error and the standard error.