Hi Anatoly,

The method Ifeffit uses to compute uncertainties in fitted parameters is independent of noise in the data because it, in essence, assumes the fit is statistically good and rescales accordingly. This means that the estimated uncertainties really aren't dependable for fits that are known to be bad (e.g. have a huge R-factor, unrealistic fitted parameters, etc.), but since those fits aren't generally the published ones, that's OK.

Secondly, the high-R amplitude will not be essentially zero with theoretically-generated data, even if you don't add noise, because the effect of having a finite chi(k) range will create some ringing even at high R.

Frankly, the default method by which Ifeffit (and Larch? I haven't used Larch) estimates the noise in the data is pretty iffy, although there's not really a good alternative. The user can override it with a value of their own, but as you know, epsilon is a notoriously squirrelly concept in EXAFS fitting. The really nice thing about the Ifeffit algorithm is that it makes the choice of epsilon irrelevant for the reported uncertainties.

What it is NOT irrelevant for is the chi-square. For this reason, I personally ignore the magnitude of the chi-square reported by Artemis, but pay close attention to differences in chi square (actually, reduced chi square) for different fits on the same data.