I've come across a problem with my least squares fits and I think someone else must have analyzed this, but I don't know where to find it. I have a converged least squares fit of my spectroscopic data. Unfortunately, the physical model, on which the fit is based, is mediocre. The deviations between measurement and model are much larger than the statistical errors at each data point. There is almost certainly nothing I can do about that. The fit reproduces the data reasonably well, but the model is incomplete. I know that there are some parameters inside the model, which do not seem to be very robust. If I fit only half of my data (only s or only p polarization), they always come out differently. Other parameters remain totally unchanged. I'm looking basically for an idea on how I could quantify this "robustness". It can probably been done based on some sort of artificial perturbation function, but I haven't seen anything like that.