Extrapolation and confounding in accelerated material degradation and failure studies

The fundamental difficulty with inference in nontrivial extrapolation where model selection is involved from a rich space of models is that any model estimated in one regime used for decision making in another is fundamentally confounded with disruptive alternatives. These are alternative models which if true would support a diametrically opposed action from the one the estimated model supports. One strategy to support extrapolation and reduce arbitrary fitting and confounding is to force the model to derive from the same mathematical structure that underlies the substantive science appropriate for the phenomena. Then statistical model fitting follows the form of theory generation in artificial intelligence, with statistical model selection tools and the statistician taking the place of the inference engine.