In search of the best approximant

In this work we want to show that, in general, a universal-best approximation does not exist, and that the choice of the approximant should be related to the context in which the approximation is needed. As we will clearly show, a good choice of approximant to compute expected values, does not necessarily remain the best choice when the context is changed, for example to approximately compute hazard rates.