Part 3 of a 3-part lecture series on input modeling for stochastic simulation. This lecture describes point estimation of parameters by maximum likelihood as well as the use of goodness-of-fit techniques (Chi-square, Kolmogorov-Smirnov, Anderson-Darling, and Shapiro-Wilk) to evaluate those best fits. It also includes a general discussion about hypothesis testing and cautionary notes about p-values.