 A closely related point is that at least some of the models described above impose requirements on explanation that may be satisfiable in some domains of inquiry but are either unachievable (in any practically interesting sense) in other domains or, to the extent that they may be achievable, bear no discernible relationship to generally accepted goals of inquiry in those domains. For example, we noted above that many scientists and philosophers hold that there are few if any laws to be discovered in biology and the social and behavioral sciences. If so, models of explanation that assign a central role to laws may not be very illuminating regarding how explanation works in these disciplines. As another example, even if we suppose that the partition into objectively homogeneous reference classes recommended by the SR model is an achievable goal in connection with certain quantum mechanical phenomena, it may be that (as suggested above) it is simply not a goal that can be achieved in a non-trivial way in economics and sociology, disciplines in which causal inference from statistics also figures prominently. In such disciplines, it may be that additional statistically relevant partitions of any population or subpopulation of interest will virtually always be possible, so that the activity of finding such partitions is limited only by the costs of gathering additional information. A similar assessment may hold for most applications of the CM model to the social sciences.