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A growing fraction of Simple Stellar Population (SSP) models, in an aim tocreate more realistic simulations capable of including stochastic variation intheir outputs, begin their simulations with a distribution of discrete starsfollowing a power-law function of masses. Careful attention is needed to createa correctly sampled Initial Mass Function (IMF) and in this contribution weprovide a solid mathematical method called MASSCLEAN IMF Sampling for doing so.We then use our method to perform $10$ $million$ MASSCLEAN Monte Carlo stellarcluster simulations to determine the most massive star in a mass distributionas a function of the total mass of the cluster. We find a maximum mass range ispredicted, not a single maximum mass. This maximum mass range is (a) dependenton the total mass of the cluster and (b) independent of an upper stellar masslimit, $M_{limit}$, for $unsaturated$ clusters and comes out naturally usingour IMF sampling method. We then turn our analysis around, now starting withour new $25$ $million$ simulated cluster database, to constrain the highestmass star from the observed integrated colors of a sample of 40 low-mass LMCstellar clusters of known age and mass. Finally, we present an analyticaldescription of the maximum mass range of the most massive star as a function ofthe cluster's total mass, and present a new $M_{max}-M_{cluster}$ relation.

The Range of Variation of the Mass of the Most Massive Star in Stellar Clusters Derived from 35 Million Monte Carlo Simulations