 An auxiliary statistical hypothesis, as part of the background \(b\), may be required to connect hypothesis \(h_i\) to the evidence. For example, a blood test for HIV has a known false-positive rate and a known true-positive rate. Suppose the false-positive rate is .05—i.e., the test tends to incorrectly show the blood sample to be positive for HIV in 5% of all cases where HIV is not present. And suppose that the true-positive rate is .99—i.e., the test tends to correctly show the blood sample to be positive for HIV in 99% of all cases where HIV really is present. When a particular patient’s blood is tested, the hypotheses under consideration are this patient is infected with HIV, \(h\), and this patient is not infected with HIV, \({\nsim}h\). In this context the known test characteristics function as background information, b. The experimental condition \(c\) merely states that this particular patient was subjected to this specific kind of blood test for HIV, which was processed by the lab using proper procedures. Let us suppose that the outcome \(e\) states that the result is a positive test result for HIV. The relevant likelihoods then, are \(P[e \pmid h\cdot b\cdot c] = .99\) and \(P[e \pmid {\nsim}h\cdot b\cdot c]\) = .05. 