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text: '[summary: A nurse is screening a set of patients for an illness, Diseasitis, using a tongue depressor that tends to turn black in the presence of the disease.\n\n- On average, around 20% of the patients in a population like this one will actually have Diseasitis. (20% prior prevalence.)\n- Among the patients with Diseasitis, 90% turn the tongue depressor black. (90% true positives.)\n- 30% of patients without Diseasitis also turn the tongue depressor black. (30% false positives.)\n\nThe probability that a patient with a blackened tongue depressor has Diseasitis can be found using [693 Bayes' rule].]\n\nA nurse is screening a student population for a certain illness, Diseasitis (lit. "inflammation of the disease").\n\n- Based on prior epidemiological studies, you expect that around 20% of the students in a screening population like this one will actually have Diseasitis.\n\nYou are testing for the presence of the disease using a color-changing tongue depressor with a sensitive chemical strip.\n\n- Among students with Diseasitis, 90% turn the tongue depressor black.\n- 30% of the students without Diseasitis will also turn the tongue depressor black.\n\nOne of your students comes into the office, takes your test, and turns the tongue depressor black.\n\nGiven only that information, what is the probability that they have Diseasitis?\n\nThis problem is used as a central example in several introductions to Bayes's Rule, including all paths in the [1zq Arbital Guide to Bayes' Rule] and the [693 High-Speed Intro to Bayes' Rule]. A simple, unnecessarily difficult calculation of the answer can be found in [55z].',
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