The whole family of statistical paradoxes like these arise, imv (and perhaps that of Pearl's) due to a failure to distinguish between statistical and explanator

The whole family of statistical paradoxes like these arise, imv (and perhaps that of Pearl's) due to a failure to distinguish between statistical and explanatory (ie., causal) analysis. The whole machinery of statistics is insensitive to whether X causes Y, Y causes X, neither cause each other, or there is an intermediate cause. To see this, take any graph with any statistical model, and flip the axes. The analysis is the same, but the direction of causation implicitly reversed. Indeed, take any of these paradoxes (esp. simpson's). Nevertheless for much of the recent history of applied statistics these distinctions have not been made (esp. because it has been developed within the experimental contexts of dubious fields). It is important to state however, that no technique or quantity of statistics is ever sufficient to credit any association with being an explanation. To credit any association with any scientific theory, and hence neither with any properties. No quantity of associative statistical research into IQ thereby implies such a thing exists (cf. the reification fallacy). To transcend statistics into science one needs experiments, those which can control variables, and hence provide a non-statistical interpretive framework which demonstrates these variables are explanatory, and hence gives credence to the theories which explain them.