  As the frontiers of applied statistics progress through increasingly complex
experiments we must exploit increasingly sophisticated inferential models to
analyze the observations we make. In order to avoid misleading or outright
erroneous inferences we then have to be increasingly diligent in scrutinizing
the consequences of those modeling assumptions. Fortunately model-based methods
of statistical inference naturally define procedures for quantifying the scope
of inferential outcomes and calibrating corresponding decision making
processes. In this paper I review the construction and implementation of the
particular procedures that arise within frequentist and Bayesian methodologies.
