The review of statistical inference was naturally separated into three main areas, including point and interval estimation and hypothesis testing. There are a great number of texts that have been published on these core subjects in statistics, with the treatment of each subject accomplished in various depths of mathematical rigor. A theoretical treatment of the classical perspective in probability theory, distribution theory and statistical inference can be found in the classic texts by Feller, Lehmann and Lehmann and Casella. The area of statistics that attempts to unify methods of evaluating estimators is called decision theory and its basic elements will be collected first. There are two foundational elements from a decision theoretic point of view that involve the concepts of utility and randomized decision rules. Both classical and Bayesian hypothesis testing methods are unified by considering an appropriate decision theory problem for a specific choice of loss function.