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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.
By moving the result cutoff value (vertical bar), the rate of false positives (FP) can be decreased, at the cost of raising the number of false negatives (FN), or vice versa (TP = True Positives, TPR = True Positive Rate, FPR = False Positive Rate, TN = True Negatives). A perfect test would have zero false positives and zero false negatives.
The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.
The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications. The four outcomes can be formulated in a 2×2 confusion matrix, as follows:
In the most basic sense, there are four possible outcomes for a COVID-19 test, whether it’s molecular PCR or rapid antigen: true positive, true negative, false positive, and false negative.
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.
Adaptive bias is the idea that the human brain has evolved to reason adaptively, rather than truthfully or even rationally, [clarification needed] and that cognitive bias may have evolved as a mechanism to reduce the overall cost of cognitive errors as opposed to merely reducing the number of cognitive errors, when faced with making a decision under conditions of uncertainty.
English: Diagram of a binary classifier separating a set of samples into positive and negative values. Some common measures of the quality of the classification: False positives: s in the area; False negatives: s in the area; Precision: fraction of s out of all values (+) in the area; Recall/Sensitivity: fraction of s in the area, out of all s ...