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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 he or she believes that greater precision implies greater accuracy (i.e., that simply because a statement is precise, it is also true). 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 range estimate aversion. The clustering illusion and the Texas sharpshooter fallacy may both be treated as relatives of precision bias. In these former fallacies, precision is mistakenly considered evidence of causation, when in fact the clustered information may actually be the result of randomness. ==External links== * (''Truth Versus Precision In Economics'', Thomas Mayer, Emeritus Professor of Economics, University of California ) * ("Less Is More: Accuracy vs. Precision In Modeling", Susan Bachman et al. ) ("Many modelers assume that building with more precision yields a more accurate model.") 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Precision bias」の詳細全文を読む スポンサード リンク
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