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False Positive
The term false positive is most commonly employed in diagnostic classification within the context of assessing test validity. The term represents a diagnostic decision in which an individual has been identified as having a specific condition (such as an illness), when, in fact, he or she does not have the condition. The term false positive is less commonly used within the context of hypothesis testing to represent a Type I error, which is defined as rejection of a true null hypothesis, and thereby incorrectly concluding that the alternative hypothesis is supported. This entry focuses on the more common use of the term false positive within the context of diagnostic decision making. The disciplines that are most likely to be concerned with the occurrence of false positives are medicine, clinical psychology, educational and school psychology, forensic psychology (and the legal system), and industrial psychology. In each of the aforementioned disciplines, critical decisions are made about human beings based on the results of diagnostic tests or other means of assessment. The consequences associated with false positives can range from a person being found guilty of a murder he or she did not commit to a much less serious consequence, such as a qualified person being erroneously identified as unsuitable for a job, and thus not being offered the job.
Basic Definitions
Within the framework of developing measuring instruments that are capable of categorizing people and/or predicting behavior, researchers attempt to optimize correct categorizations (or predictions) and minimize incorrect categorizations (or predictions). Within the latter context, the following four diagnostic decisions are possible (the first two of which are correct and the latter two incorrect): true positive, true negative, false positive, false negative. The terms true and false in each of the aforementioned categories designate whether or not a diagnostic decision made with respect to an individual is, in fact, correct (as in the case of a true positive and a true negative) or incorrect (as in the case of a false positive and a false negative). The terms positive and negative in each of the aforementioned categories refer to whether or not the test result obtained for an individual indicates he or she has the condition in question. Thus, both a true positive and false positive represent individuals who obtain a positive test result—the latter indicating that such individuals have the condition in question. On the other hand, both a true negative and a false negative represent individuals who obtain a negative test result—the latter indicating that such individuals do not have the condition in question.
In the discipline of medicine, the true positive rate for a diagnostic test is referred to as the sensitivity of the test—that is, the probability that a person will test positive for a disease, given the person actually has the disease. The true negative rate for a diagnostic test is referred to as the specificity of the test—that is, the probability that a person will test negative for a disease, given the person actually does not have the disease. As a general rule, in order for a diagnostic test to be a good instrument for detecting the presence of a disease, it should be high in both sensitivity and specificity. The proportion of true positives and false positives in a population is referred to as the selection ratio because it represents the proportion of the population that is identified as possessing the condition in question.
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