The Principle of Optimality is examined informally (bereft of notation) in the context of discounted Markov decision processes. The optimality equations and optimality criterion are introduced with discounted Markov decision processes. The Principle of Optimality is given and interpreted. A counter example to the Principle is given for the case of the usual definition of optimality. By redefining optimality as in Hinderer  , the Principle can be verified, although possibly at some sacrifice. The paper concludes that such verification is unnecessary to justify results found in applications which use the optimality equations and criterion, since these have been established for a variety of fairly general models.