Suppose an unborn child tests positive for trisomy 21 from a cell-free fetal DNA (cfDNA) test. What is the probability that the child does have trisomy 21, given the positive test result? This conditional probability, P (D|T + ), where D is the event of having the disease and T + is the event of a positive test, has a specific name: positive predictive value (PPV) . Similarly, the negative predictive value (NPV), P (Dc |T − ), is the probability that disease is absent when test results are negative. These values are of particular importance to clinicians in assessing patient health. The characteristics of diagnostic tests are given with the reverse conditional probabilities—the probability that the test correctly returns a positive test in the presence of disease and a negative result in the absence of disease. The probability P (T + |D) is referred to as the sensitivity of the test and P (T − |Dc ) is the specificity of the test.1 Given these two values and the marginal probability of disease P (D), also known as the prevalence, it is possible to calculate both the PPV and NPV of a diagnostic test. – Sensitivity: Of 1,000 children with trisomy 21, approximately 980 test positive. – Specificity: Of 1,000 children without trisomy 21, approximately 995 test negative. – Prevalence: Trisomy 21 occurs with a rate of approximately 1 in 800 births. There are several possible strategies for approaching this type of calculation: 1) creating a contingency table for a large, hypothetical population, 2) running a simulation, 3) using an algebraic approach with Bayes’ Theorem.