To check whether our results were affected by the frequency of data collection, we subsampled 1 out of every 100 data points. Reducing the number of data points increased the ratio of SD Trajs (⟨∆G⟩ [n 0 ,N ] ) to Bias Trajs (⟨∆G⟩ [n 0 ,N ] ), shifting the optimal truncation points to slightly earlier times (Figure ). As the subsampling interval was a large fraction of the length of the autocorrelation functions used to generate the data (Table ), this also dramatically reduced the autocorrelation of the time series. It was therefore unsurprising that all generalised MSER methods, which differ only in their treatment of autocorrelation, performed similarly on all subsampled trajectory ensembles (Table ). The truncation time distributions for all methods became similar: those from the "uncorrelated estimate" generally shifted later, and the truncation time distributions from the initial sequence methods became narrower and shifted earlier. Unlike with the "standard" data, no methods produced RMSEs dramatically greater than the fixed truncation time minimum. For these reasons, it may seem tempting to subsample all time series before applying the heuristics. However, this is problematic -if the mean is calculated from the truncated subsampled data, it will have an substantially higher RMSE than for the original data, due to the loss of information in the discarded samples. Applying the truncation time from the subsampled data is also ill-advised because the subsampled data have earlier optimal truncation times than original data; the truncation times for most methods will be biased towards erroneously early times.