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1) Bayes’ Theorem relates P(A|B) to P(B|A), weighted by prior probabilities.
2) Bayes’ Theorem is useful only when reversing conditional probabilities (e.g., from test accuracy to disease probability).
3) Bayes’ Theorem requires knowing unconditional probabilities of all possible evidence outcomes.
4) In a rare disease test, even a highly accurate test can give many false positives because prevalence is low.
5) Bayes’ Theorem can be written as: P(A|B) = P(B|A)·P(A) / P(B).
6) Bayes’ Theorem cannot be applied if P(B)=0.
7) In the paper factory example, to compute P(flaw | A) we must use Bayes’ Theorem.
8) Bayes’ Theorem combines prior probability with likelihood to form the posterior probability.
9) Bayes’ Theorem is only valid if events A and B are independent.
10) In diagnostic testing, the positive predictive value (PPV) is the same as the test’s sensitivity.