Agreed. I'm studying statistics at the moment and I'm continually reminded of how easy it is to choose the wrong model / distribution and be incorrect because of some non-obvious and technical reason. For example, just the other day, I wanted to use the binomial distribution to solve a problem. To use this distribution, the trials must be independent of one another. In that particular problem, there was a subtle condition that made the trials non-independent. I arrived at correct-appearing answers (0 <= P <= 1) that were actually all wrong. Statistics is way too easy to break to be used naively.