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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.
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.
The probability that an observed positive result is a false positive may be calculated using Bayes' theorem. False negatives produce serious and counter-intuitive problems, especially when the condition being searched for is common.
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.
The probability for false positives varies by each type of home test, but Ellume specifically says on its online FAQs that "there is a chance that this test can give a positive result that is ...
The intersect operation satisfies a weaker property: the false positive probability in the resulting Bloom filter is at most the false-positive probability in one of the constituent Bloom filters, but may be larger than the false positive probability in the Bloom filter created from scratch using the intersection of the two sets.
False positive COVID-19 tests—when your result is positive, but you aren’t actually infected with the SARS-CoV-2 virus—are a real, if unlikely, possibility, especially if you don’t perform ...
A false positive Covid-19 test result can happen, but it’s rare, says Brian Labus, Ph.D., M.P.H., assistant professor at the University of Nevada Las Vegas School of Public Health.