While stochastic simulation methods predict reaction rates that can vary with a visible amount of simulation noise, in general we observe that this noise has a negligible effect on predicted copolymerization data. Increasing the size of stochastic simulations could reduce noise at a cost of increased computational load but with no increase in precision between methods (Figures and). To further assess the agreement between methods, we simulated 200 randomly generated sets of input parameters with all three methods and calculated the sum of square residuals at 100 points across the simulated conversion range. Between the three methods, three possible sum of squared residual (SSR) values were calculated, and the largest value is reported in Figure , sorted by the type of copolymerization behavior as determined by reactivity ratios heuristics (all values are listed in Table ). Over all conditions and copolymerization behaviors, the precision of these methods is excellent, producing SSR values < 10 -3 , representative of at most a 0.32% deviation in monomer conversion predictions at all points. For comparison, 1% deviation between values, which is often less than experimental uncertainty, would give an SSR value of 10 -2 . Notably, these SSR values are significantly lower than those obtained from fitting experimental data sets, supporting the claim that such discrepancies do not affect the ability of one or all methods to fit real experimental data (vide infra). Consistency between monomer conversion data and relative reaction rates was observed in all cases explored. C. The maximum sum of square residuals (SSR) between the three models when comparing across various copolymerization behaviors, denoted by color. SSR values were found to be less than 10 -3 (corresponding to an R 2 value of >0.999), which is an order of magnitude lower than the value obtained with a 1% difference in prediction at each of the 100 data points. These data confirm that all models are precise and accurate over the reactivity space explored.