In this lecture, we review summary statistics, MLE, and goodness-of-fit tests (particularly Chi-square and Kolmogorov–Smirnov, with some mention of Anderson–Darling and Shapiro–Wilk), with a particular focus on the type-I error, type-II error, and statistical power. We then introduce verification, validation, and calibration of simulation models and close with an example for the simulation of a bank. We use rigorous statistical methods to drive the calibration process that leads to updating the model of the bank and ensuring its outputs are a good statistical match for outputs in a real bank. This involves making use of a power analysis for a one-sample, two-sided t-test. We will cover the paired t-test version of this problem in the next lecture.
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