Following the strategy of the Monte Carlo simulation, the introduced error can be calculated from the standard normal distribution with (a) a defined variance or (b) via bootstrapping. To test the influence of different approaches on the confidence interval, a set of simulations were performed on the given dataset with the optimised 1:1/1:2 model. The standard normal distributed errors were generated with σ MC = SE y , σ MC = σ Fit , σ MC = 1e -3 , σ MC = 2e -3 , σ MC = 3e -3 and σ MC = 5e -3 . Monte Carlo simulation with T = 100, 200, 300, 500, 700, 1000, 1500, 2000, 2500, 3000 and 5000 steps were performed, where each simulation was repeated 300 times. The 95% confidence interval was then characterised by the median and standard deviation of the 0.95 interpercentile ranges (IPR) for these 300 Monte Carlo simulations. The boxplots and the standard deviation of the 0.95 IPR values for the stability constants lgK 11 and lgK 12 after the Monte Carlo simulation are reported in Fig. and show expected behaviour: With increasing steps T, the observed standard deviation of the IPR decreases. The same trend is visible for the other Monte Carlo simulations including BS (see Fig. ). With increasing step count the IPR converges to the ideal IPR that could be obtained after an infinite number of steps. As Efron stated, at least 2000 steps are required for the bootstrap method to obtain reliable results. However, since every Monte Carlo step requires the least-squares estimation of θ , this approach is demanding. As shown in Fig. , Monte Carlo simulation scales well with the number of threads used and benefits from Hyperthreading technology. ‡ ‡ Therefore, accurate Monte Carlo simulation with 2000 † † This is expected as they resample the original normal distributed random numbers. ‡ ‡ The benchmark was obtained on a i9-7920X CPU with 12 cores overlocked to 4.00GHz, using openSUSE 15.0 Leap. SupraFit was compiled using gcc 7.4.1. steps can easily be obtained within minutes even on a desktop computer with fewer cores.