 Now suppose that the regions A and B almost completely overlap, but neither is contained within the other (see  Figure 5(b)).  Again, the corresponding events A and B will be correlated, and the earlier common cause may not screen them off. (Arntzenius (1999 [2010: section 2.4]) has an example that has essentially this structure.) It seems that this case, too, is one in which the events A and B are insufficiently distinct. But now it becomes difficult to formulate a notion of distinctness that is sufficiently general, without ruling out too much. To see the problem, imagine that instead of a literal dartboard, we have a Venn diagram representing the possible states of a system that evolves indeterministically. The spatial regions A and B now represent sets of possible states, corresponding to the states in which events A and B occur. And the area corresponds to probability. Thus the set of states in which A occurs almost completely overlaps with the set of states in which B occurs, as measured by the probability distribution over states. Is this a reason to think that A and B are not distinct? It had better not be, because this is just a representation of the generic case where events A and B are probabilistically correlated. Thus spelling out the precise notion of distinctness at work remains a challenge.