  Besides the well-known effect of autocorrelations in time series of Monte
Carlo simulation data resulting from the underlying Markov process, using the
same data pool for computing various estimates entails additional cross
correlations. This effect, if not properly taken into account, leads to
systematically wrong error estimates for combined quantities. Using a
straightforward recipe of data analysis employing the jackknife or similar
resampling techniques, such problems can be avoided. In addition, a covariance
analysis allows for the formulation of optimal estimators with often
significantly reduced variance as compared to more conventional averages.
