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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.
In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses ) that are false (incorrect rejections of ...
In statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm rate [1]) is the probability of falsely rejecting the null hypothesis for a particular test.
Using the sequential analysis ("online") approach, any change test must make a trade-off between these common metrics: . False alarm rate; Misdetection rate; Detection delay; In a Bayes change-detection problem, a prior distribution is available for the change time.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
V is the number of false positives (Type I error) (also called "false discoveries") S is the number of true positives (also called "true discoveries") T is the number of false negatives (Type II error) U is the number of true negatives = + is the number of rejected null hypotheses (also called "discoveries", either true or false)
Medical tests may be designed to minimise the number of false negatives (type II errors) produced by loosening the threshold of significance, raising the risk of obtaining a false positive (a type I error). The rationale is that it is better to tell a healthy patient "we may have found something—let's test further," than to tell a diseased ...
In that case, a fixed threshold level can be chosen that provides a specified probability of false alarm, governed by the probability density function of the noise, which is usually assumed to be Gaussian. The probability of detection is then a function of the signal-to-noise ratio of the target return. However, in most fielded systems ...