In population A, a person who receives a positive test can be more than 93% confident (400/30-400) that he or she correctly indicates an infection. Imagine testing for infectious disease in an A population of 1,000 people, 40% of whom are infected. The test has a false positive rate of 5% (0.05) and has no false negative rate. The expected result of the 1000 population A screening tests would be that an example of the basic interest rate error is the false positive paradox. This paradox describes situations where there are more false positives than real positives. For example, 50 out of 1,000 people test positive infection, but only 10 have the infection, which means that 40 tests have been false positive. The probability of a positive result is determined not only by the accuracy of the test, but also by the properties of the random population. [2] If prevalence, the proportion of those with a given condition is lower than the wrong positive test rate, even tests, which have very little chance of giving a false positive on a case-by-case basis, will overall yield more false results than real positive results. [3] The paradox surprises most people. [4] The forward rate agreement, abbreviated FRA, is one of the most widely used financial instruments in the world of finance.

It is concluded between two counterparties, over-the-counter. In population B, only 20 people out of a total of 69 are actually infected with a positive result. The probability of becoming infected after saying you are infected is only 29% (20/20 -49) for a test that appears to be “95% accurate”. It is particularly counter-intuitive to interpret a positive result in a test on a low-prevalence population after treating positive results from a high-prevalence population. [3] If the false positive rate of the test is greater than the proportion of the new population with the disease, then a test administrator whose experience has been derived from tests in a high-prevalence population may infer from experience that a positive test result is generally indicative of a positive reason, although there was a falsely positive probability. Therefore, the probability that one of the drivers among the 1 -49.95 – 50.95 positive results is really drunk is 1 / 50.95 ≈ 0.019627 .displaystyle 1/50.95 `about 0.019627`.