A false positive is the dismissal or rejection of a null hypothesis when the hypothesis is true. False positives are also common in security systems. A false positive is an error in some evaluation process in which a condition tested for is mistakenly found to have been detected. A false positive is where you receive a positive result for a test, when you should have received a negative results. False positive is sometimes called a “false alarm” or “false positive error.” When trying to identify dangerous offenders researchers often make mistakes. One of these mistakes is known as a false positive.
The false positives error is identifying someone as dangerous, and possibly keeping them incarcerated or denying them parole, when they are not dangerous. The opposite of false positives would be a false negative: identifying someone as non-dangerous when they in fact go on to commit a dangerous act.
A false positive is usually
used in the medical field, but it can also apply to other arenas like software
testing. Some examples of false positives:
A pregnancy test is positive, when in fact you aren’t pregnant.
A prenatal test comes back positive for Down’s Syndrome, when actually your fetus does not have the disorder.
A cancer screening test comes back positive, but you don’t have the disease.
Virus software on your computer incorrectly identifies a harmless program as a malicious one.
False-positive in Tuberculin Skin Test. Bacille Calmette-Guérin vaccine, BCG vaccination as it is referred to as, may cause a false-positive reaction to the TST, which may complicate decisions about prescribing treatment. Blood tests to detect TB infection, unlike the TST, are not affected by prior BCG vaccination and are less likely to give a false-positive result. BCG vaccination can cause a false positive Mantoux test, a test for immunity to tuberculosis using intradermal injection of tuberculin, although a very high-grade reading is usually due to active disease. World Health Organization does not recommend BCG vaccination for the prevention of COVID-19 for lack of evidence.
When you have a test that can say "Yes" or "No" as a medical test, it just
It could be wrong when it says "Yes".
It could be wrong when it says "No".
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 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 disease, when in reality it is present. They are also known in medicine as a false positive diagnosis or false negative diagnosis. A false positive is distinct from overdiagnosis, and is also different from overtesting.
The probability that an observed positive result is a false positive (as contrasted with an observed positive result being a true positive) may be calculated using Bayes' theorem. The key concept of Bayes' theorem is that the true rates of false positives and false negatives are not a function of the accuracy of the test alone, but also the actual rate or frequency of occurrence within the test population; and, often, the more powerful issue is the actual rates of the condition within the sample being tested.
Type Error, Tyoe I errors and Type II errors are very often referred to as false positives and false negatives respectively. The terms are now commonly applied in much wider and far more general sense than Neyman and Pearson's original specific usage, as follows:
False positives in Spam
While most anti-spam tactics can block or filter a high percentage of unwanted emails, doing so without creating significant false-positive results is a much more demanding task. A false negative occurs when a spam email is not detected as spam, but is classified as non-spam.