The number of histogram bins also effects the estimated rate of convergence for a given sample size: larger numbers of bins will magnify differences between the distributions, effectively making them noisier for a given number of decorrelated samples and yielding a stricter convergence criterion whereas smaller numbers of bins will blur these differences and accelerate convergence acceptance. If one requires a minimum of more decorrelated samples than our current threshold of 50, the number of bins could be increased; however, we note that others have found diminishing statistical accuracy with increased sampling due to the tendency of these simulations to inefficiently sample the entire ensemble of configurations, leading to large autocorrelation times and therefore resulting in expensive acquisition of decorrelated samples. Finally, some simulations may require large amounts of simulation time to obtain convergence, which may be greater than the computational budget allows. Balancing between the computational budget and allowance for long simulations is controlled by the maximum simulation length hyperparameters, which should be adjusted according to one's computational budget. In instances when convergence is difficult to achieve in the budgeted simulation length, more advanced methods such as Hamiltonian exchange variants 10,46,69 may be necessary; however, their evaluation is beyond the scope of this work. We note that our method is applicable to any application of stratified time series analysis beyond protein-ligand binding free energy simulations, including solvation free energy, amino acid mutations, and protein-protein binding free energy.