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Scientific research and understanding are more selectively focused than traditional conceptions of a comprehensive scientific image suggest. Most truths and possible topics of inquiry have little or no scientific significance; recognizing which concepts, projects, apparatus, skills, and achieved results matter scientifically is central to scientific understanding. This chapter adapts Davidson’s distinction between homonomic and heteronomic inquiry to explicate two complementary aspects of scientific significance. The most basic form of conceptual articulation in the sciences is homonomic, extending and refining domain-constitutive concepts, skills, and laws. A self-enclosed domain of concepts and skills would be conceptually empty, however. Internal development of conceptual domains matters in its bearing upon other scientific domains and broader concerns. Heteronomic inquiry ranges from local collaborative projects, through more stable “trading zones”, to new domains of inquiry at, between, or across the boundaries of others. The chapter uses examples mostly from the history of genetics to show how homonomic and heteronomic conceptual development are closely intertwined in shaping the significance of research projects and achievements. Neither a comprehensive scientific image nor conceptions of an unsurpassable disunity of science adequately account for this futural aspect of the temporality of conceptual normativity in the sciences.

Nietzsche’s suggestion that with the emergence and eventual demise of the human intellect, “nothing will have happened”, makes vivid a worry raised by naturalistic accounts of human conceptual capacities. Many naturalistically inclined thinkers still maintain a vestigial loyalty to transcendent notions of rationality or conceptual normativity, to avert the worry that scientific naturalism would self-defeatingly undermine the significance of scientific understanding. The epilogue argues that neither the evolutionary contingency of our conceptually articulated space of reasons nor the historical contingency of scientific understanding undermines the authority or significance of the “scientific image” as re-conceived in the book. Naturalism does not require transcendent conceptions of reason or science, but can account for their normativity from within our biologically and historically evolving way of life.

This chapter takes on the problem of model adequacy and makes an argument for reformulating the way model-based statistical inference is carried out. In the new formulation, it does not treat the model as “truth.” It is instead an approximation to truth. Rather than testing for model fit, an integral part of the proposed statistical analysis is to assess the degree to which the model provides adequate answers to the statistical questions being posed. One method for doing so is to create a single overall measure of inadequacy that evaluates the degree of departure between the model and truth. The chapter argues that there are two components of errors in any statistical analysis. One component is due to model misspecification; that is, the working model is different from the true data-generating process. The chapter compares confidence intervals on model misspecification error with external knowledge of the scientific relevance of prediction variability to address the issue of scientific significance. The chapter also analyzes several familiar measures of statistical distances in terms of their possible use as inadequacy measures.