153:, 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.
367:
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
259:
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
132:
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
216:
is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false negatives that reject the alternative hypothesis when it is true).
109:, 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.
294:= 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
49:
is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a
189:(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. The false positive rate depends on the
228:(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.
306:-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
125:, 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.
497:
Alnabulsi, Hussein; Islam, Rafiqui; Mamun, Qasi (2018). "A novel algorithm to protect code injection attacks". In
Abawajy, Jemal H.; Choo, Kim-Kwang Raymond; Islam, Rafiqul (eds.).
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Bose, Prosenjit; Guo, Hua; Kranakis, Evangelos; Maheshwari, Anil; Morin, Pat; Morrison, Jason; Smid, Michiel; Tang, Yihui (2008). "On the false-positive rate of Bloom filters".
286:, 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
310:= 0.05 in a single experiment, we would have to be 87% certain that there was a real effect before the experiment was done to achieve a false positive risk of 5%.
347:
499:
International
Conference on Applications and Techniques in Cyber Security and Intelligence: Applications and Techniques in Cyber Security and Intelligence
302:= 0.001 would have a false positive rate of 8 percent. It wouldn't even reach the 5 percent level. As a consequence, it has been recommended that every
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in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a
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occurring in a test where a single condition is checked for, and the result of the test is erroneous, that the condition is absent.
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Cronin, Paul; Kelly, Aine Marie (2011). "Influence of population prevalences on numbers of false positives: an overlooked entity".
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264:. Corrections for multiple comparisons aim only to correct the type I error rate, so the result is a (corrected)
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Colquhoun, David (2018). "The false positive risk: A proposal concerning what to do about p values".
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Robinson, Alexander; Keller, L. Robin; del Campo, Cristina (October 2022).
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Banerjee, A; Chitnis, UB; Jadhav, SL; Bhawalkar, JS; Chaudhury, S (2009).
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450:"The reproducibility of research and the misinterpretation of p-values"
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271:. Thus they are susceptible to the same misinterpretation as any other
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402:"Building insights on true positives vs. false positives: Bayes' rule"
501:. Cham, Switzerland: Springer International Publishing. p. 288.
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Ambiguity in the definition of false positive rate, below
105:, where a positive result corresponds to rejecting the
30:"False Positive" redirects here. For other uses, see
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of a real effect being 0.1, even the observation of
255:Ambiguity in the definition of false positive rate
406:Decision Sciences Journal of Innovative Education
524:"Hypothesis testing, type I and type II errors"
348:Why Most Published Research Findings Are False
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342:Positive and negative predictive values
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27:Types of error in binary classification
101:, the analogous concepts are known as
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235:, this fraction is given the letter
389:False Positives and False Negatives
284:error of the transposed conditional
282:Confusion of these two ideas, the
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320:Receiver operating characteristic
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32:False Positive (disambiguation)
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368:and the fewer false positives.
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179:False positive rate
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602:Academic Radiology
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