ⓘ False positives and false negatives. In medical testing, and more generally in binary classification, a false positive is an error in data reporting in which a ..

                                     

ⓘ False positives and false negatives

In medical testing, and more generally in binary classification, a false positive is an error in data reporting in which a test result improperly indicates presence of a condition, such as a disease, when in reality it is not present, while a false negative is an error in which a test result improperly indicates no presence of a condition, when in reality it is present. These are the two kinds of errors in a binary test They are also known in medicine as a false positive diagnosis, and in statistical classification as a false positive error. A false positive is distinct from overdiagnosis, and is also different from overtesting.

In statistical hypothesis testing the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and statistical hypothesis testing.

                                     

1. False positive error

A false positive error, or in short a false positive, commonly called a false alarm ", is a result that indicates a given condition exists, when it does not. For example, in the case of "The Boy Who Cried Wolf", the condition tested for was "is there a wolf near the herd?"; the shepherd at first wrongly indicated there was one, by calling "Wolf, wolf!"

A false positive error is a type I error where the test is checking a single condition, and wrongly gives an affirmative positive decision. However it is important to distinguish between the type 1 error rate and the probability of a positive result being false. The latter is known as the false positive risk see Ambiguity in the definition of false positive rate, below.

                                     

2. False negative error

A false negative error, or in short a false negative, is a test result that indicates that a condition does not hold, while in fact it does. In other words, erroneously, no effect has been inferred. An example for a false negative is a test indicating that a woman is not pregnant whereas she is actually pregnant. Another example is a truly guilty prisoner who is acquitted of a crime. The condition "the prisoner is guilty" holds the prisoner is indeed guilty. But the test a trial in a court of law failed to realize this condition, and wrongly decided that the prisoner was not guilty, falsely concluding a negative about the condition.

A false negative error is a type II error occurring in a test where a single condition is checked for and the result of the test is erroneously that the condition is absent.

                                     

3.1. Related terms False positive and false negative rates

The false positive rate 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 statistical hypothesis testing, this fraction is given the Greek letter α, and 1−α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but raises the probability of type II errors false negatives that reject the alternative hypothesis when it is true.

Complementarily, the false negative rate is the proportion of positives which yield negative test outcomes with the test, i.e., the conditional probability of a negative test result given that the condition being looked for is present.

In statistical hypothesis testing, this fraction is given the letter β. The "power" or the "sensitivity" of the test is equal to 1−β.



                                     

3.2. Related terms Ambiguity in the definition of false positive rate

The term false discovery rate FDR was used by Colquhoun 2014 to mean the probability that a "significant" result was a false positive. Later Colquhoun 2017 used the term false positive risk FPR for the same quantity, to avoid confusion with the term FDR as used by people who work on multiple comparisons. Corrections for multiple comparisons aim only to correct the type I error rate, so the result is a corrected p value. Thus they are susceptible to the same misinterpretation as any other p value. The false positive risk is always higher, often much higher, than the p value. Confusion of these two ideas, the error of the transposed conditional, has caused much mischief. Because of the ambiguity of notation in this field, it is essential to look at the definition in every paper. The hazards of reliance on p -values was emphasized in Colquhoun 2017 by pointing out that even an observation of p = 0.001 was not necessarily strong evidence against the null hypothesis. Despite the fact that the likelihood ratio in favor of the alternative hypothesis over the null is close to 100, if the hypothesis was implausible, with a prior probability of a real effect being 0.1, even the observation of p = 0.001 would have a false positive rate of 8 percent. It wouldnt even reach the 5 percent level. As a consequence, it has been recommended that every p value should be accompanied by the prior probability of there being a real effect that it would be necessary to assume in order to achieve a false positive risk of 5%. For example, if we observe p = 0.05 in a single experiment, we would have to be 87% certain that there as a real effect before the experiment was done to achieve a false positive risk of 5%.

                                     

4. Consequences

In many legal traditions there is a presumption of innocence, as stated in Blackstones formulation:

"It is better that ten guilty persons escape than that one innocent suffer."

That is, false negatives a guilty person is acquitted and goes unpunished are far less adverse than false positives an innocent person is convicted and suffers. This is not universal, however, and some systems prefer to jail many innocent, rather than let a single guilty escape – the tradeoff varies between legal traditions.

                                     
  • criminal. False positive and false negative See more information in: False positive and false negative In terms of false positives and false negatives a positive
  • positives are not overlooked so false negatives are few and specificity is the extent to which actual negatives are classified as such so false positives
  • False color or pseudo color refers to a group of color rendering methods used to display images in color which were recorded in the visible or non - visible
  • the elements that are not correctly classified are named false positives and false negatives Some classification rules are static functions. Others can
  • identify bad code. cpplint.py suffers from both false positives and false negatives False positives can be eliminated by tagging lines with NOLINT
  • A false accusation of rape is the reporting of a rape where no rape has occurred. With regard to the rape allegations made to police or campus authority
  • false positive rate, even tests that have a very low chance of giving a false positive in an individual case will give more false than true positives
  • A false memory is a psychological phenomenon where a person recalls something that did not happen or happened differently from the way it actually happened
  • A false flag is a covert operation designed to deceive the deception creates the appearance of a particular party, group, or nation being responsible