Youden's J Statistic

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Bol Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Youden's J statistic (also called Youden's index) is a single statistic that captures the performance of a diagnostic test. The use of such a single index is "not generally to be recommended". It is equal to the risk difference for a dichotomous test. Sensitivity and specificity are statistical measures of the performance of a binary classification test. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are identified as having the condition). Specificity measures the proportion of negatives which are correctly identified (e.g. the percentage of healthy people who are identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A theoretical, optimal prediction can achieve 100% sensitivity (i.e. predict all people from the sick group as sick) and 100% specificity (i.e. not predict anyone from the healthy group as sick).

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Youden's J statistic (also called Youden's index) is a single statistic that captures the performance of a diagnostic test. The use of such a single index is "not generally to be recommended". It is equal to the risk difference for a dichotomous test. Sensitivity and specificity are statistical measures of the performance of a binary classification test. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are identified as having the condition). Specificity measures the proportion of negatives which are correctly identified (e.g. the percentage of healthy people who are identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A theoretical, optimal prediction can achieve 100% sensitivity (i.e. predict all people from the sick group as sick) and 100% specificity (i.e. not predict anyone from the healthy group as sick).

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