False positives and false negatives

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"False Positive" redirects here. For other uses, see False Positive (disambiguation).

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 (the result is positive), when in reality it is not, while a false negative is an error in which a test result improperly indicates no presence condition (the result is negative), when in reality it is present. These are the two kinds of errors in a binary test, and are contrasted with a correct result, either a true positive or a true negative. These are also known in medicine as a false positive diagnosis (resp. false negative diagnosis), and in statistical classification as a false positive error (resp. false negative error).


False positive error[edit]



False negative error[edit]

A false negative error, or in short false negative, is where a test result indicates that a condition failed, while it actually was successful. I.e. erroneously no effect has been assumed. A common example is a guilty prisoner freed from jail. The condition: "Is the prisoner guilty?" is true (yes, the prisoner is guilty). But the test (a court of law) failed to realize this, and wrongly decided the prisoner was not guilty.


Related terms[edit]

False positive and false negative rates[edit]






Receiver operating characteristic[edit]



Both types of errors are problems for individuals, corporations, and data analysis. In testing for a medical condition, a false positive in medicine (a condition being detected when none exists) causes unnecessary worry or treatment, while a false negative (a condition going undetected when it is present) gives the patient the dangerous illusion of good health and the patient might not get an available treatment. In testing for defective products, a false positive in manufacturing quality control (classifying a product as defective when it is well made) discards a product that is actually well made, while a false negative stamps a broken product as operational. A false positive in scientific research suggests an effect that is not actually there, while a false negative fails to detect an effect that is there.

Based on the real-life consequences of an error, one type may be more serious than the other. In many applications there is a trade-off between these errors, particularly when classifying based on a threshold: a lower threshold for positive results yields more false positives but fewer false negatives.

For example, in high-cost or life-and-death situations, like space exploration or military equipment, the cost of defects is very high (a mission fails or someone dies), and thus one has very strict tolerances. Thus NASA engineers would prefer to waste some money and throw out an electronic circuit that is really fine (false positive) than to throw out less but use one on a spacecraft that is actually broken (false negative). In this situation false positives use more money but increase mission safety, but a false negative would save some money but would risk the entire mission.

On the other hand, in many legal traditions there is a presumption of innocence, as stated in Blackstone's formulation that:

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

that is, that false negatives (a guilty person is acquitted and escapes) are far preferable to false positive (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.

Notwithstanding, Remitigate LLC developed a sophisticated software program to help clinicians (and patients) decipher unexpected or false negative and positive results of the IA urine drug screen test. It allows clinicians to enter the daily doses of patient medications, the result of each test (i.e. positive or negative opiates, benzodiazepines, etc.), and the final screen gives guidance on whether or not the result was expected based on drug interactions, prescribed drug (i.e. synthetic, natural, or semisynthethic opioid), and advises the next step which could be definitive testing by chromatograpthy. Dr. Jeffrey Fudin on remitigate.com or direct access at urintel.com




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