Block averaging is another method which reduces autocorrelation between consecutive data points. However, unlike subsampling, it does not affect the underlying bias-standard deviation trade-off of the time series, and the optimal fixed-time truncation points remain the same. As discussed in Section 2.3, estimation of the variance of the mean by block averaging is closely related to estimating the variance of the mean using window estimators with a Bartlett window. Hence, block averaging the data before analysis effectively turns the "uncorrelated estimate" into a window estimator, and increases the effective window sizes of the window size estimators. However, we repeated the tests after block averaging all "standard" time series (block size 100) as a way of simulating less correlated and less noisy data. Block averaging dramatically improved the performance of the "uncorrelated estimate" on the late truncation point systems (Figure and Table ), which was unsurprising given that this effectively became a window method with a large window. Generally, the distributions of discard times became wider and shifted to later times for all methods. For the early truncation point systems, this generally worsened the performance of the window estimators so that they became comparable with the initial sequence estimators. In terms of RMSE, the initial sequence methods usually did not perform significantly worse than without block-averaging. The bimodal discard time distributions observed for the initial sequence methods on the "standard" dataset were observed for the window estimators for some systems.