Consider the Bayesian Table that analyzes a test for a disease that reports a "positive" or "negative" result where "positive" indicates the subject has the disease and a "negative" indicates the subject does not have the disease.The pretest probability (prior probability) for a disease is the probability that a subject selected at random from the population has the disease.The sensitivity of a test is the probability the test is positive given the subject has the disease which is the same as "1-(False Negative Probability)" or essentially a "true positive".The specificity of a test is the probability the test is negative given the subject does not have the disease which is the same as "1-(False Positive Probability)" or essentially a "true negative".

A disease has a pretest probability of 0.20.

A test for the disease has a sensitivity of 0.85 (85%) and a specificity of 0.75 (75%).

How many statements are correct?01234

Statement 1. The probability a subject has the disease after a positive test result is approximately 0.46

Statement 2. The probability a subject does not have the disease after a negative test result is approximately 0.95

Statement 3. The probability a subject has the disease after a two consecutive positive test results is approximately 0.74

Statement 4. The probability a subject does not have the disease after a positive test result following a negative test result is approximately 0.85