This paper discusses the notion of optimality for time-average MDPs. We argue that while most authors claim to use the "average reward" criteria, the notion that is implicitly used is in fact the notion of what we call Bellman optimality. We show that it does not coincide with other existing notions of optimality, like gain-optimality and bias-optimality but has strong connection with canonical-policies (policies that are optimal for any finite horizons) as well as value iteration and policy iterations algorithms.