We observed a general trade-off: methods which more thoroughly accounted for autocorrelation often showed late and variable truncation times; methods which less thoroughly accounted for autocorrelation often showed early truncation, relative to the optimal fixedtime truncation point. This increased variance and bias, respectively. We found that using a window estimator of the variance with size N n 0 provided a good compromise between these extremes, generally providing robust truncation point estimates. Rerunning our analyses on noisier and less correlated data produced similar conclusions.