We have shown that the size of transformation does not affect the accuracy of results in general. We have used transformations with the absolute difference between the number of disappearing and appearing atoms up to 21 and found that the results are almost as accurate as those for the entire dataset. Thus, the standard protocol of using 13 λ windows is sufficient for such large transformations. In addition, we have employed our modified dual topology scheme in this study as it overcomes the issues associated with both single as well as dual topology schemes and combines the pros of them both. It has been successfully employed on a large dataset in this study further confirming its general applicability. An online tool to automatically build models with this topology scheme for TIES calculations is freely available for anyone to use. Non-equilibrium methods based on MD are also known to exhibit dominant aleatoric uncertainties in the same way as equilibrium methods discussed in this study. Although non-equilibrium free energy methods such as PMX have not been investigated within this paper, our findings and previous studies indicate that it is necessary to perform similar systematic analyses for these methods too. By the same token, machine learning methods are strongly data-driven and hence the underlying distributions of data must be accounted for in such methods to properly control the accuracy and/or precision of their predictions.