That is an inappropriate way of checking your assumptions. Assuming a normal is assuming a model (a model is a simplification of a situation by only considering the relative parts). Writing a simulation is also assuming a model, although the process is a lot less formal than traditional statistical regression.

If a modeler has assumed two different models of a situation and are getting vastly different results then that is evidence something is wrong, but it is also evidence that the modeler is out of their depth. It is not appropriate to fit two different models and then claim that the differences are delivering insight. The differences are revealing big gaps in the modelers understanding of key influences, rendering both models highly suspect. They should not be creating models, they should be putting more time into understanding the thing they are modeling.

There are times when a modeler would have two different models, but they should certainly not be surprised that they give different results. Indeed, they should be assuming two different models because they are going to give completely different results. There will probably be other edge cases, but in my experience they are rarer than people just making mistakes about where MCM is appropriate.