Our bootstrapping results described above indicated the 10 x 10 and 10 x 20 protocols to consistently provide a good balance of precision and low compute resources when calculating the activation free energies. In the following section we therefore focused on these two protocols and applied random sub sampling to evaluate how the number of replicas used would be expected to alter the 95% CIs of the results obtained for each enzyme. Random subsampling is a variant of bootstrapping with replacement whereby each resample of the data is made up of less observations than the total distribution, which in this case allowed us to predict the extent to which the CIs would reduce as more and more observations/repeats are used per sample. The results from our random subsampling analysis on the calculated ΔG ‡ are shown in Figure and on the calculated ΔG0 are show in Figure . We note that the average value from each subsample is the product of 5000 resamples of the dataset and therefore would not be expected to alter with changing numbers of replicas. Across all 3 systems and replicas, the 95% CIs show a similar pattern in which increasing replicas leads to improved precision, and after approx. 5 replicas per system the change in CIs becomes rather small (Figure ). For example, 1 replica in PTP1B has a range between the lower and upper bound of the 95% CIs of 3.3 kcal mol -1 , which decreases to 1.3 kcal mol -1 after 5 replicas and then to 1.0 kcal mol -1 after 10 replicas in the 10 x 20 protocol. Likewise, this observation is also borne out in MtPriA, with 1 replica giving a range between 6 kcal mol -1 , which decreases to 2.6 kcal mol -1 after 5 replicas and then to 1.9 kcal mol -1 after 10 replicas. Finally, 1 replica gives a range of 7.1 kcal mol -1 , which decreases to 3.1 kcal mol -1 after 5 replicas and then to 2.2 kcal mol -1 after 10 replicas. Given the above observations, it could be reasonable to perform and/or compute resources are limited. Regardless, our subsampling results clearly demonstrate the needs for repeats in order to generate precise predictions of ΔG ‡ for all three model systems.