Two common heuristics for automated truncation point selection in molecular simulations are those of Yang et al. and Chodera. Yang et al. proposed a method based on reverse cumulative averaging, where a final "equilibrated" portion of the data are assumed to be Gaussian. The "equilibrated" region is extended in the reverse direction until significant deviation from this distribution is detected, indicating "unequilibrated" data. However, the assumption of normality is not always justified, and the robust selection of an intial "equilibrated" region may be challenging. Chodera suggested selecting the truncation point which maximises the effective sample size, demonstrating the method on a simple system with rapid convergence and short correlation times. However, this method is prone to selecting late truncation points for more correlated data from more realistic applications. There is a substantial literature on truncation point selection outside the field of molecular simulation. In particular, the marginal standard error rule (MSER) family of methods has been found to be simple, effective, and easily automatable. These methods select the truncation point by minimising the marginal standard error, and are very similar to Chodera's method of maximising the effective sample size; the main difference is that the MSER methods popular in operational research account for correlation less thoroughly, if at all, and their applicability to correlated molecular simulation data is uncertain.