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In order to have a quantitative estimate of how well the distributions of fit parameters match expectations, a KS test is used. IdeallyIn order to apply this test, we would have an need the expected distribution for a given fit parameter, centered on or with its mode located at the value that was input to the simulations. It is not clear what this expected distribution should be, so here we make the assumption that it is a Gaussian function centered on the input value and having the same root-variance as the distribution of fitted values from the MC trials. This is not a conservative assumption, but useful in this context as it provides the best possible KS probability for any given set of trials, i.e., the real situation is worse than what is presented here.
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