Type I and type II errorsIn statistical hypothesis testing, a type I error is the mistaken rejection of an actually true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the failure to reject a null hypothesis that is actually false (also known as a "false negative" finding or conclusion; example: "a guilty person is not convicted").
Gold standard (test)In medicine and statistics, a gold standard test is usually the diagnostic test or benchmark that is the best available under reasonable conditions. In other words, a gold standard is the most accurate test possible without restrictions. The meanings may differ in the two fields, because in medicine with some conditions only an autopsy guarantees diagnostic certainty, thus the gold standard test would be the best one that keeps the patient alive instead of the autopsy.
Point-of-care testingPoint-of-care testing (POCT), also called near-patient testing or bedside testing, is defined as medical diagnostic testing at or near the point of care—that is, at the time and place of patient care. This contrasts with the historical pattern in which testing was wholly or mostly confined to the medical laboratory, which entailed sending off specimens away from the point of care and then waiting hours or days to learn the results, during which time care must continue without the desired information.
Lift (data mining)In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target () is much better than the baseline () average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response.
Cohen's kappaCohen's kappa coefficient (κ, lowercase Greek kappa) is a statistic that is used to measure inter-rater reliability (and also intra-rater reliability) for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation, as κ takes into account the possibility of the agreement occurring by chance. There is controversy surrounding Cohen's kappa due to the difficulty in interpreting indices of agreement.
