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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]
The continuum between the extremes is ignored. The term probability neglect was coined by Cass Sunstein. [1] There are many related ways in which people violate the normative rules of decision making with regard to probability including the hindsight bias, the neglect of prior base rates effect, and the gambler's fallacy. However, this bias is ...
The following are forms of extension neglect: 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]
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 may be neglected more often when the information presented is not causal. [17] 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]
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 ...
Based on an estimated 7,032 incidents a year, study authors determined that on average one battery-related trip to the emergency room occurs every 1.25 hours in the U.S.
Insensitivity to sample size is a cognitive bias that occurs when people judge the probability of obtaining a sample statistic without respect to the sample size.For example, in one study, subjects assigned the same probability to the likelihood of obtaining a mean height of above six feet [183 cm] in samples of 10, 100, and 1,000 men.