Hi, When using data in calculations, errors can be introduced by the calculations themselves; A common example of this kind of data is the atomic mass of some element; The data might be reported by NIST as: 190.233(20), which indicates that the mean is 190.233, and the maximum likelihood deviation is 0.020. Data from NIST is always assumed to be GAUSSIAN. But doing operations on Gaussians (eg: ×,/ ) can produce resulting distributions that are not Gaussians: png 1, example multiply png 2, example multiply Generally, people ignore this problem and just assume the result *is* still a Gaussian (not to mention that people typically don't use the exact formula for getting the sigma and mu of multiplication) Hence the error can compound rapidly as more operations are performed. I'd like to do something a little better (but not much.), I'd like to create a single composite PDF/CDF that replaces the Gaussian and corrects somewhat for these introduced errors. It is NOT practical to compute the exact CDF/PDF for a series of operations, but only for one or two. Hence, I want to design a single CDF/PDF (call it O) that can "optimize" the error. Each operation's result would be measured (somehow) to produce the best fit parameters for a near-equivalent "O" distribution. I am planning to keep only mu, sigma, skew, and possibly kurtosis at each step as definitive of a unique "O" Example: Given three uncorrelated Gaussians u,v; and and three "O" variables p o w Now: consider the following calculations: LET: o=u × v; p=o + w, p=o × w, and p=o / w I'd like the errors of the second set of operations to be about the same, and in some sense minimized. What metric would be appropriate to simultaneously minimize the errors of the second set of equations ? I can produce what I think is a decent CDF/PDF if necessary.