The second last column of Table contains the fraction of complexes (in % terms) with error bars (σ) greater than 0.5 kcal/mol which is 43%/68% in case of ES3, and reduces to 5%/54% in case of ES10 with intermediate values for ES5 and ES8 for EQ/NEQ. The last column contains the fraction of complexes (in % terms) with zero overlap (in case of NEQ) that reduces from 72% for ES3 to 49% ES10. All this conclusively demonstrates that both accuracy and precision improve substantially with increasing ensemble size and that reporting results from a single replica is not reliable. We note that ensemble simulations based only on varying initial conditions address the aleatoric uncertainty but cannot eradicate any systematic bias prevalent in the system due to factors such as force field inaccuracies, errors in experimental data and so on. This is generally true for any enhanced sampling method, as also reported in the case of REST2 in previous studies . The bias due to differences in conformations (such as binding poses) may be overcome by performing ensemble simulations with varying initial structures. The other factors responsible for the systematic bias are captured by the uncertainties in the epistemic parameters, namely those which can be improved by gathering more data and/or improving the theoretical understanding of the system/method. Ensemble simulations can be used to control epistemic uncertainty in order to alleviate systematic bias. It is a major undertaking and computationally demanding which is out of the scope of the current study. However, in the present study, all such biases are encompassed in the empirical constant proposed here (see the Supporting Information for details). Vassaux et al. and Edeling et al. show how to use ensembles to control epistemic uncertainties.