 A second, closely related point is that the SR model departs from the IS model in abandoning the idea that a statistical explanation of an outcome must provide information from which it follows the outcome occurred with high probability. As the reader may check, the statement of the SR model above imposes no such high probability requirement; instead, even very unlikely outcomes will be explained as long as the criteria for SR explanation are met. Suppose that, in the above example, the probability of quick recovery from strep, given treatment and the presence of a non-resistant strain, is rather low (e.g., 0.2). Nonetheless, if the criteria (i)–(iii) above—a homogeneous partition with correct probability values for each cell in the partition—are satisfied, we may use this information to explain why x, who had a non-resistant strain of strep and was treated, recovered quickly. Indeed, according to the SR model, we may explain why some x which is A is B, even if the conditional probability of B given A and the cell \(C_i\) to which x belongs \((p_i = P(B\pmid A.C_i))\) is less than the prior probability \((p = P(B\pmid A))\) of B in A. For example, if the prior probability of quick recovery among all those with any form of strep is 0.5 and the probability of quick recovery of those with a resistant strain who are untreated is 0.1, we may nonetheless explain why y, who meets these last conditions \(({-T}.R),\) recovered quickly (assuming he did) by citing the cell to which he belongs, the probability of recovery given that he falls in this cell, and the other sort of information described above. More generally, what matters on the SR model is not whether the value of the probability of the explanandum-outcome is high or low (or even high or low in comparison with its prior probability) but rather whether the putative explanans cites all and only statistically relevant factors and whether the probabilities it invokes are correct. One consequence of this, which Salmon endorses while acknowledging that many will regard it as unintuitive, is that on the SR model, the same explanans E may explain both an explanandum M and explananda that are inconsistent with M, such as \(-M\). For example, the same explanans will explain both why a subject with strep and certain other properties (e.g., T and \(-R\)) recovers quickly, if he does, and also why he does not recover if he does not. By contrast, on the DN or IS models, if E explains M, E cannot also explain \(-M\).