Abuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.

The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.

Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.

Statistics may be a principled means of debate with opportunities for agreement, but this is true only if the parties agree to a set of rules. Misuses of statistics violate the rules.

Or to put it another way:

False facts are highly injurious to the progress of science, for they often long endure; but false views, if supported by some evidence, do little harm, as every one takes a salutary pleasure in proving their falseness; and when this is done, one path towards error is closed and the road to truth is often at the same time opened.

— Charles Darwin, The Descent of Man (1871), Vol. 2, 385.

One usable definition is: "Misuse of Statistics: Using numbers in such a manner that - either by intent, or through ignorance or carelessness - the conclusions are unjustified or incorrect." The "numbers" include misleading graphics discussed elsewhere. The term is not commonly encountered in statistics texts and no authoritative definition is known. It is a generalization of lying with statistics which was richly described by examples from statisticians 60 years ago.

The definition confronts some problems (some are addressed by the source):

How to Lie with Statistics acknowledges that statistics can legitimately take many forms. Whether the statistics show that a product is "light and economical" or "flimsy and cheap" can be debated whatever the numbers. Some object to the substitution of statistical correctness for moral leadership (for example) as an objective. Assigning blame for misuses is often difficult because scientists, pollsters, statisticians and reporters are often employees or consultants.

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