We have attempted to create synthetic data sets with varied autocovariance functions and biases. Across our data sets, the N n 0 window method favourably compromised bias sensitivity and discard time variability, and appeared reasonably robust. By increasing the variability of our data, we increased confidence that this applies to higher variance data (Section 4.3); by decreasing the autocorrelation (Section 4.4) we increased confidence that this applies to less correlated data. However, we acknowledge our data's coverage of possible time series generated from molecular simulations remains limited.