False Negative
Definition
A false negative is a type of error in which a test incorrectly indicates that something is not present, when it actually is. This type of error is also known as a Type II error, or a miss. In the medical field, false negatives may result in a patient receiving a false sense of security, leading to a delay in diagnosis of a potentially serious condition.
History
False negatives have been studied in the field of statistics since the early 20th century. The concept was made popular by the work of statistician Ronald Fisher. Fisher proposed the idea of a test that would produce two types of errors: false positives and false negatives. He argued that a false positive should be less serious than a false negative, since it would only result in unnecessary anxiety and expense for a patient.
Characteristics
False negatives occur when a test returns a negative result, but the condition being tested for is actually present. This is the opposite of a false positive, which occurs when a test result is positive but the condition being tested for is not present. False negatives are more serious than false positives, as they can lead to a delay in diagnosis and treatment of a serious medical condition.
False negatives can be caused by a variety of factors, including the sensitivity of the test, the quality of the testing procedure, and the accuracy of the sample being tested. For example, if a sample is not taken correctly, or if it is contaminated, it may result in a false negative.
Conclusion
False negatives are a type of error that occurs when a test incorrectly indicates that something is not present, when it actually is. This type of error can be caused by a variety of factors, including the sensitivity of the test, the quality of the testing procedure, and the accuracy of the sample being tested. False negatives are more serious than false positives, as they can lead to a delay in diagnosis and treatment of a serious medical condition.
References
Fisher, R. (1925). Statistical methods for research workers. London: Oliver & Boyd.
Kirkwood, B. R. (2011). Essential medical statistics (2nd ed.). Chichester, UK: Wiley-Blackwell.
Lau, J., Ioannidis, J. P. A., & Terrin, N. (2006). The false-negative rate of a test: From the definition to estimation. Statistics in Medicine, 25(17), 2815-2829. doi:10.1002/sim.2349
Miller, T. D., & Miller, M. S. (2014). Clinical epidemiology: The essentials (6th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.