 \(\Pr_1\) and \(\Pr_2\) are just the most extreme cases. From the fact that Ted was exposed to asbestos and developed lung cancer, we know that he is either sensitive or doomed. The counterfactual of interest will be true just in case he is sensitive. Hence the probability of the counterfactual, given the available evidence, is P(sensitive | sensitive or doomed). However, using \(\Pr_1\) yields a conditional probability of .45 (5/11), while \(\Pr_2\) yields a conditional probability of 1. Given the information available to us, all we can conclude is that the probability of necessity is between .45 and 1. To determine the probability more precisely, we would need to know the probability distribution of the error variable.