Precision bias
Encyclopedia
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
.
Cognitive bias
A cognitive bias is a pattern of deviation in judgment that occurs in particular situations. Implicit in the concept of a "pattern of deviation" is a standard of comparison; this may be the judgment of people outside those particular situations, or may be a set of independently verifiable...
in which an evaluator of information commits a logical fallacy
Fallacy
In logic and rhetoric, a fallacy is usually an incorrect argumentation in reasoning resulting in a misconception or presumption. By accident or design, fallacies may exploit emotional triggers in the listener or interlocutor , or take advantage of social relationships between people...
as the result of confusing accuracy and precision
Accuracy and precision
In the fields of science, engineering, industry and statistics, the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's actual value. The precision of a measurement system, also called reproducibility or repeatability, is the degree to which...
. 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
Clustering illusion
The clustering illusion refers to the tendency erroneously to perceive small samples from random distributions to have significant "streaks" or "clusters", caused by a human tendency to underpredict the amount of variability likely to appear in a small sample of random or semi-random data due to...
and the Texas sharpshooter fallacy
Texas sharpshooter fallacy
The Texas sharpshooter fallacy is a logical fallacy in which pieces of information that have no relationship to one another are called out for their similarities, and that similarity is used for claiming the existence of a pattern. This fallacy is the philosophical/rhetorical application of the...
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
Randomness
Randomness has somewhat differing meanings as used in various fields. It also has common meanings which are connected to the notion of predictability of events....
.
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.")