How to choose the best distribution for a stochastic model when having minimal datapoints to work with In a simulation model that must reflect the process of a real environment (hospital, airport etc.) how can one choose the best distribution for the various stochastic processes within the model when only having 25-35 datapoints? Typically, a triangular distribution is used when empirical data is scarce, however, when the data is put into a histogram, vague resemblance of a lognormal, gamma, weibull, distribution is shown. My question is, what can be done as due diligence with such little data to ensure that the ideal distribution is chosen? Or does one simply proceed with triangular?



Use descdist from fitdistrplus package in R. It will provide a skewness-kurtosis plot that shows what distribution your data is most likely to have. You can use the bootstrap option for scarce data. If it sounds confusing just run it and it is very intuitive once you see the results. Then perform the relevant test for candidate distribution(s) to see if it fits your data meaningfully (you can use goft package in R).
