Hi everyone, I agree 99.99% with Bruce's explanation, and just want to clarify a couple points and go on about a few other aspects. As Bruce points out, the estimated error bars are rescaled because the automated estimate of the uncertainty in the data is almost always too small, and that the reported reduced chi-square for a good fit often exceeds 20 (whereas it should be close to 1). Bruce also points out (again, correctly) that we have very little experience training monkeys. Sorry, I couldn't resist. Chi-square, Reduced Chi-square, and the R-factor can be used to determine whether a fit is 'good'. They can certainly be used to compare two fits to decide which is 'better'. In Statistics 101 (ie, a first pass at discussing Data and Error Analysis), Chi-square is used to estimate uncertainties in fitted parameters, provided the uncertainties in the data are known. That is, in Statistics 101, Chi-square has two very different purposes: 1) Is the fit good? and 2) What are the uncertainties in the parameters? Since, we don't know the uncertainties in the data (which Statistics 101 happily ignores), we have to do the best we can. The approach used by ifeffit is common and carefully critiqued in Numerical Recipes by Press, et al. This approach definitely leads to the caution Bruce expressed about using some judgment about whether to trust fit results. The rescaling means that the reported uncertainties in the parameters are valid *IF* the fit is "good". That seems reasonable because if the fit is not "good", you probably don't care that the reported uncertainties are not good either. Incidentally, the Statistics 101 view of Chi-square also glosses over the notion of what counts as a 'data point'. That leads to the whole idea of Number of Independent Points, N_idp, which would set a maximum number of fittable parameters and would go into the Chi-square equation, and has led to lots of discussion in the EXAFS community. If non-rescaled Chi-square is used to estimate uncertainties, N_idp would also effect the error bars. Rescaling error bars to assert that the fit is good (that is, scaling epsilon so that Chi-square = N_idp - N_Parameters) actually lessens the dependence of the error bars on N_idp. The 'white-noise' estimate of epsilon_R that Ifeffit (and Feffit) does is very easy, and usually not too far off for white-noise (that is, the portion of the noise that is independent of R). It actually works reasonably well for very noisy data. We all do the best we can to avoid that situation!! Bruce gave the normal arguments of saying that the white-noise estimate doesn't include systematic errors. I would put a slight variation on this: It does include systematic and statistical errors that are 'white', but doesn't include statistical or systematic errors that are not white. There has been a lot of speculation in the EXAFS community about the importance of systematic errors. Many have suggested that systematic errors are dominated by bad background-removal. I'm not sure I agree, but this would certainly count as a non-white, systematic error. Glitches are systematic errors that have a fairly large component that is white. I don't think anyone really has a complete handle on this topic, or indeed why EXAFS fits tend to be much worse than white-noise would predict. Blaming the Feff calculations is another popular option!! In principle, a Bayesian approach could help, but I don't think that it would magically give better error bar estimates. In a Bayesian approach, we would need to put uncertainties on the Feff calculations too -- a good idea, but not trivial to do. That being said, if anyone has any ideas of a better approach or even a robust alternative, please let me know!! --Matt