What is the difference between sensitivity analysis and model validation? I read both wikipedia pages of sensitivity analysis and model validation (here, only linear regression validation) but I don't manage to find a way to separate these two terms.
I have the impression that the first one is more used in academia and engineering in general and the second one in "data science".
One option I see is to modify the level of description of these terms: sensitivity analysis is more like a general terms to design a high level branch of methods, and model validation may be more specific and be include in sensitivity analysis.
Any thought?
I am more interested in the difference than in the similarities between these two notions.
 A: In addition, sensitivity analysis can be considered as a tool to enhance model validity by choosing proper values(calibration) for the most critical input parameters.
By using sensitivity analysis and defining input parameters values, we add more credibility to the model in hand.
A: This is a bit of an oversimplification, but model validation generally tells one about how well the current model fits the data at hand.
Sensitivity analyses tell one how likely your results based upon that model would change given new information or changes to your assumptions.
For example, someone could develop a model aimed at determining the impact that an intervention has on an outcome, and that model could validate well under their collected data (i.e. it seems very good at predicting response).  However, that model rests on a number of assumptions – one being that all covariates are accounted for. A sensitivity analysis could tell one how much your model results would change if this new, "imaginary" variable, with certain properties, existed.
