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In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α 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 ...
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. [ 1 ]
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).
The specificity of the differential-susceptibility effect is demonstrated if the model is not replicated when other susceptibility factors (i.e., moderators) and outcomes are used. Finally, the slope for the susceptible subgroup should be significantly different from zero and at the same time significantly steeper than the slope for the non ...
Prominent examples of such domain-general views include Jean Piaget’s theory of cognitive development, and the views of many modern connectionists. Proponents of domain specificity argue that domain-general learning mechanisms are unable to overcome the epistemological problems facing learners in many domains, especially language.
The biopsychological theory of personality is a model of the general biological processes relevant for human psychology, behavior, and personality. The model, proposed by research psychologist Jeffrey Alan Gray in 1970, is well-supported by subsequent research and has general acceptance among professionals.