<unk>  - The left half of the image with the solid dots represents individuals who have the condition, while the right half of the image with the hollow dots represents individuals who do not have the condition. The circle represents all individuals who tested positive.<unk>  mathematically describe the accuracy of a test which reports the presence or absence of a condition. Individuals for which the condition is satisfied are considered "positive" and those for which it is not are considered "negative".<unk>  (True Positive Rate) refers to the probability of a positive test, conditioned on truly being positive.<unk>  (True Negative Rate) refers to the probability of a negative test, conditioned on truly being negative. If the true condition can not be known, a "Gold Standard test" is assumed to be correct. In a diagnostic test,<unk>  is a measure of how well a test can identify true positives and<unk>  is a measure of how well a test can identify true negatives. For all testing, both diagnostic and screening, there is usually a trade-off between<unk> , such that higher<unk> es will mean lower<unk> es and vice versa. If the goal of the test is to identify everyone who has a condition, the number of false negatives should be low, which requires high<unk> . That is, people who have the condition should be highly likely to be identified as such by the test. This is especially important when the consequence of failing to treat the condition are serious and/or the treatment is very effective and has minimal side effects. If the goal of the test is to accurately identify people who do not have the condition, the number of false positives should be very low, which requires a high<unk> . That is, people who do not have the condition should be highly likely to be excluded by the test. This is especially important when people who are identified as having a condition may be subjected to more testing, expense, stigma, anxiety, etc.<unk>  The terms<unk> " and<unk> " were introduced by American biostatistician Jacob Yerushalmy in 1947.
Sensitivity and specificity
Positive and negative predictive values
Indicative limit value
Absolute threshold
Likelihood ratios in diagnostic testing
Inter-rater reliability
Threshold limit value
Relative value unit