All tests are imperfect: Accounting for false positives and false negatives using Bayesian statistics

All tests are imperfect: Accounting for false positives and false negatives using Bayesian statistics

Tests with binary outcomes (e.g., positive versus negative) to indicate a binary state of nature (e.g., disease agent present versus absent) are common. These tests are rarely perfect: chances of a false positive and a false negative always exist. Imperfect results cannot be directly used to infer t...

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Bibliographic Details
Main Authors: Song S. Qian, Jeanine M. Refsnider, Jennifer A. Moore, Gunnar R. Kramer, Henry M. Streby
Format: Article
Language:English
Published: Elsevier 2020-03-01
Series:Heliyon
Subjects:
Conditional probability
False negative
Uncertainty
False positive
Bayes' rule
Statistics
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844020304163

Internet

http://www.sciencedirect.com/science/article/pii/S2405844020304163

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