 In general, Bayesian (relevance) confirmation theory implies that the evidential import of an instance of some generalization will often depend on the credence structure, and relies on its formal representation, \(P\), as a tool for more systematic analyses. Consider another instructive example. Assume that \(a\) denotes some company from some (otherwise unspecified) sector of the economy, and label the latter predicate \(S\). So, \(k = Sa\). You are informed that \(a\) increased revenues in 2019, represented as \(e = Ra\). Does this confirm \(h = \forall x(Sx \rightarrow Rx)\)? It does, at least to some degree, one would say. For an expansion of the whole sector (recall that you have no clue what this is) surely would account for the data. That’s a straightforward HD kind of reasoning (and a suitable Hempelian counterpart reconstruction would concur). But does \(e\) also confirm \(h^* = Sb \rightarrow Rb\) for some further company \(b\)? Well, another obvious account of the data \(e\) would be that company \(a\) has gained market shares at the expenses of some competitor, so that \(e\) might well seem to support \(\neg h^*,\) if anything (the revenues example is inspired by a remark in Blok, Medin, and Osherson 2007, 1362).