Some of this evidence is included among the examples here discussed. One reasonable starting point for understanding a complex process is therefore to determine whether it is composed of such modules, and if so, try to identify them. Ultimately this should lead to analysis of the functions of each such module, as well as an understanding of how they work together. In this paper I have explored the idea of separate modifiability as a criterion for modularity, by considering the shared and distinguishing features of a set of diverse examples. Such evidence consists of pure measures that are selectively influenced by the factors, or of a composite measure for which the effects of the factors combine appropriately.
Selective influence is also, in itself, evidence for the modules being functionally distinct.
By considering such examples together, within a unified structure that highlights their inferential logic, we come to understand them better, and applications in one area benefit from lessons learned in another. Attempts to make the inferential logic explicit should help in thinking about what a set of findings may mean, and may suggest preferences for some kinds of experiment over others. More generally, failure of the prediction of a joint hypothesis may be less informative than success. Implications of such invariance are sufficiently strong that it should not be asserted simply because the evidence against it is unconvincing. Though not required with pure measures, factorial experiments permit testing the generality of findings of invariance.