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The base rate fallacy, also called base rate neglect [2] or base rate bias, is a type of fallacy in which people tend to ignore the base rate (e.g., general prevalence) in favor of the individuating information (i.e., information pertaining only to a specific case). [3]
For example, if the control group, using no treatment at all, had their own base rate of 1/20 recoveries within 1 day and a treatment had a 1/100 base rate of recovery within 1 day, we see that the treatment actively decreases the recovery. The base rate is an important concept in statistical inference, particularly in Bayesian statistics. [2]
Base rates are used less if there is relevant individuating information. [18] Groups have been found to neglect base rate more than individuals do. [19] Use of base rates differs based on context. [20] Research on use of base rates has been inconsistent, with some authors suggesting a new model is necessary. [21]
Base rate fallacy or base rate neglect, the tendency to ignore general information and focus on information only pertaining to the specific case, even when the general information is more important. [47] Compassion fade, the tendency to behave more compassionately towards a small number of identifiable victims than to a large number of ...
Base rate heuristic: When a decision involves probability this is a mental shortcut that uses relevant data to determine the probability of an outcome occurring. When using this Heuristic there is a common issue where individuals misjudge the likelihood of a situation.
Exemplification theory is a simple combination of these heuristics. It posits that since exemplars come to mind more easily than base-rates when accessing information, available exemplars will dominate base-rate information when making judgments of event populations. [6] Behavioral intentions are the direct determinants of behaviors. [7]
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In cognitive psychology and decision science, conservatism or conservatism bias is a bias which refers to the tendency to revise one's belief insufficiently when presented with new evidence. This bias describes human belief revision in which people over-weigh the prior distribution ( base rate ) and under-weigh new sample evidence when compared ...