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An example of the base rate fallacy is the false positive paradox (also known as accuracy paradox). This paradox describes situations where there are more false positive test results than true positives (this means the classifier has a low precision). For example, if a facial recognition camera can identify wanted criminals 99% accurately, but ...
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]
A base rate is a phenomenon's basic rate of incidence. The base rate fallacy describes how people do not take the base rate of an event into account when solving probability problems. [12] This was explicitly tested by Dawes, Mirels, Gold and Donahue (1993) who had people judge both the base rate of people who had a particular personality trait ...
Base rate fallacy – making a probability judgment based on conditional probabilities, without taking into account the effect of prior probabilities. [ 6 ] Conjunction fallacy – the assumption that an outcome simultaneously satisfying multiple conditions is more probable than an outcome satisfying a single one of them.
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 ...
Thus, people can overestimate the likelihood that something has a very rare property, or underestimate the likelihood of a very common property. This is called the base rate fallacy. Representativeness explains this and several other ways in which human judgments break the laws of probability. [14]
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The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.