Precision bias

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Precision bias is a form of cognitive bias in which an evaluator of information commits a logical fallacy as the result of confusing accuracy and precision. More particularly, in assessing the merits of an argument, a measurement, or a report, an observer or assessor falls prey to precision bias when they believe that greater precision implies greater accuracy (i.e., that simply because a statement is precise, it is also true); the observer or assessor are said to provide false precision.

Precision bias, whether called by that phrase or another, is addressed in fields such as economics, in which there is a significant danger that a seemingly impressive quantity of statistics may be collected even though these statistics may be of little value for demonstrating any particular truth.

It is also called the numeracy bias or the interval estimate aversion.

The clustering illusion and the Texas sharpshooter fallacy may both be treated as relatives of precision bias. In these related fallacies, precision is mistakenly considered evidence of causation, when in fact the clustered information may actually be the result of randomness.

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