On Wednesday, May 11, 2011 11:45:43 pm Brandon Reese wrote:
Does this mean that reporting reduced chi-square values in a paper that compared several data sets would not be necessary and/or appropriate?
Heavens! No! That we don't have a reliable way of estimating epsilon says that we cannot apply the standard criterion for recognizing a good fit (i.e. in Gaussian statistics, a reduced chi-square of 1 indicates a good fit). That is, in Ifeffit/Artemis, reduced chi-square for a single fit cannot be interpretted. However, reduced chi-square can be used to assert that one fitting model is an improvement on another fitting model. If reduced chi-square gets significantly smaller, then the second fitting model can be said to be an improvement over the first. So, if the point of a paper is to say that your sample behaves *this* way and not *that* way, one of the tools available to you for making that argument is that the data are more consistent with *this* model because its reduced chi-square is significantly smaller than for *that* model. Of course, reduced chi-square can only be compared for fitting models which compute epsilon the same way or use the same value for epsilon.
Would setting a value for epsilon allow comparisons across different k-ranges, different (but similar) data sets, or a combination of the two using the [reduced] chi-square parameter?
Yup. What you wrote wasn't strictly wrong, but considering the reduced chi-square lets you also compare fits with different variable parameters. B -- Bruce Ravel ------------------------------------ bravel@bnl.gov National Institute of Standards and Technology Synchrotron Methods Group at NSLS --- Beamlines U7A, X24A, X23A2 Building 535A Upton NY, 11973 My homepage: http://xafs.org/BruceRavel EXAFS software: http://cars9.uchicago.edu/~ravel/software/exafs/