Bootstrapping with replacement is a statistical technique which enables one to estimate summary statistics for a dataset. Here, we used bootstrapping to generate probability densities representing the distribution of the calculated ΔG ‡ and ΔG0 values obtained from our simulations for the six different sampling protocols investigated for each system. For each sampling protocol and system, we performed 5000 bootstrap resamples on the calculated ΔG ‡ and ΔG0 values (separately) to generate 5000 average ΔG ‡ and ΔG0 values. These 5000 bootstrapped averaged values obtained for each scenario and system were then converted to probability densities using a Gaussian kernel density estimation with 25 equally spaced bins. The lower and upper boundary range for the probability densities was kept consistent for each system and was determined by the minimum and maximum bootstrap sample value obtained for any of the six scenarios for that given system.