False positives and false negatives
A false positive is an error in
In
False positive error
A false positive error, or false positive, is a result that indicates a given condition exists when it does not. For example, a pregnancy test which indicates a woman is pregnant when she is not, or the conviction of an innocent person.[citation needed]
A false positive error is a
False negative error
A false negative error, or false negative, is a test result which wrongly indicates that a condition does not hold. For example, when a pregnancy test indicates a woman is not pregnant, but she is, or when a person guilty of a crime is acquitted, these are false negatives. The condition "the woman is pregnant", or "the person is guilty" holds, but the test (the pregnancy test or the trial in a court of law) fails to realize this condition, and wrongly decides that the person is not pregnant or not guilty.[citation needed]
A false negative error is a
Related terms
False positive and false negative rates
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.[citation needed]
The false positive rate is equal to the
In
Complementarily, the false negative rate (FNR) 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
Ambiguity in the definition of false positive rate
The term false discovery rate (FDR) was used by Colquhoun (2014)
Confusion of these two ideas, the
Receiver operating characteristic
The article "Receiver operating characteristic" discusses parameters in statistical signal processing based on ratios of errors of various types.
See also
- Base rate fallacy
- False positive rate
- Positive and negative predictive values
- Why Most Published Research Findings Are False
Notes
- ^ When developing detection algorithms or tests, a balance must be chosen between risks of false negatives and false positives. Usually there is a threshold of how close a match to a given sample must be achieved before the algorithm reports a match. The higher this threshold, the more false negatives and the fewer false positives.
References
- ^ False Positives and False Negatives
- ^ PMID 29308247.
- PMID 21180491.
- ^ PMID 26064558.
- ^ Colquhoun, David. "The problem with p-values". Aeon. Aeon Magazine. Retrieved 11 December 2016.
- S2CID 85530643.