Statistical ratios | Statistical classification
Sensitivity and specificity 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". * Sensitivity (true positive rate) refers to the probability of a positive test, conditioned on truly being positive. * Specificity (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, sensitivity is a measure of how well a test can identify true positives and specificity 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 sensitivity and specificity, such that higher sensitivities will mean lower specificities and vice versa. If the goal is to return the ratio at which the test identifies the percentage of people highly likely to be identified as having the condition, the number of true positives should be high and the number of false negatives should be very low, which results in high sensitivity. This is especially important when the consequence of failing to treat the condition is serious and/or the treatment is very effective and has minimal side effects. If the goal is to return the ratio at which the test identifies the percentage of people highly likely to be identified as not having the condition, the number of true negatives should be high and the number of false positives should be very low, which results in high specificity. That is, people 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. The terms "sensitivity" and "specificity" were introduced by American biostatistician Jacob Yerushalmy in 1947. (Wikipedia).
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Ever wandered how to calculate sensitivity, specificity, positive and negative predictive values or odds ratios or even simply what these terms mean? Watch this short lecture.
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https://www.patreon.com/edmundsj If you want to see more of these videos, or would like to say thanks for this one, the best way you can do that is by becoming a patron - see the link above :). And a huge thank you to all my existing patrons - you make these videos possible. In this video
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Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is and give examples of the sensitivity of a test. The sensitivity of a test indicates the probability that the subject will have a POSITIVE result when the subject is actually POSITIVE.
From playlist PROB & STATS 4 BAYES THEOREM
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From playlist StatQuest
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In this StatQuest we talk about Sensitivity and Specificity - to key concepts for evaluating Machine Learning methods. These make it easier to choose which method is best for your data. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you
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