Scripts corresponding to the Monte Carlo bootstrap analysis of the referred paper. The scripts use particle size distributions (PSDs) measured with a LISST-200X laser diffraction instrument to determine the optimum measurement time. The optimal measurement time is a trade of between accuracy and time investment. In the script, the PSD is read from raw LISST output data making use of the provided functions. Next, it randomly drews a subset of measurements and calculated its D50. The size of the subset ranged from one measurement to all measurements in the entire set. Next, a Monte Carlo bootstrap analysis is performed 1000 times for each subset size to determine the deviation of the subset from the data set mean D50. The minimum and maximum values were taken from each run. The measurement frequency (which could be more than 1 measurement per second) can be used to convert the number of measurements, as calculated by the Monte Carlo bootstrap analysis, to measurement time. By studying the change in maximum deviation from the data set mean when adding more measurement readings (when measurement time increases), one can give an estimate on how many readings (and hence measurement time) were needed to give a representative estimate of the D50 of the sample.