  When a large body of data from diverse experiments is analyzed using a
theoretical model with many parameters, the standard error matrix method and
the general tools for evaluating errors may become inadequate. We present an
iterative method that significantly improves the reliability of the error
matrix calculation. To obtain even better estimates of the uncertainties on
predictions of physical observables, we also present a Lagrange multiplier
method that explores the entire parameter space and avoids the linear
approximations assumed in conventional error propagation calculations. These
methods are illustrated by an example from the global analysis of parton
distribution functions.
