I'm not sure to which extent your questions makes sense, but I'm pretty sure the answer I'm about to give is not the one you want.
You can use Kolmogorov-Smirnov (or Anderson-Darling I suppose) to check if your data (i.e. the empirical distribution) is significantly different from your modeled distribution. However, my attitude in general is that if the K-S test is the answer, you probably asked the wrong question.
One problem with this approach is that if you have a lot of data, K-S will probably reject the model even if it's a fairly good model; and if you have little data, K-S will probably accept the model unless it is a particularly bad fit.
However, the truly important question to ask, and which this approach does not address, is (usually, and for whatever question you are originally interested in):
Is there an alternative model which fits the data equally well (or
roughly so), but which would otherwise lead you to a different
Without knowing the details of what the original question is, it's hard to be more concrete.