I was having a discussion with a statistics professor a while ago about the different 'flavours' of statistics (frequentist, Bayesian, ...). He posed that he would subdivide statistics in four categories: non-parametric-, robust-, frequentist- and Bayesian statistics. The subdivision is characterized by the amount of assumption the methods make about underlying distributions (non-parametric statistics makes none, while Bayesian makes those assumptions very explicit).
I was going to to ask if CrossValidated agrees with this subdivision, but since that is a subjective question I'll ask:
1) Is this subdivision widely recognized in statistics;
2) do 'real world' problem usually require one particular method? Ie, given some problem, is there a method most suitable for solving that problem or can multiple methods work for a given problem?
Thanks in advance.