During this lecture slot, we start with slides from Lecture G3 (on goodness of fit) that were missed during the previous lecture due to timing. In particular, we review hypothesis testing fundamentals (type-I error, type-II error, statistical power, sensitivity, false positive rate, true negative rate, receiver operating characteristic, ROC, alpha, beta) and then go into examples of using Chi-squared and Kolmogorov–Smirnov tests for goodness of fit for arbitrary distributions. We also introduce Anderson–Darling (for flexibility and higher power) and Shapiro–Wilk (for high-powered normality testing).
We close with where we originally intended to start – with definitions of testing, verification, validation, and calibration. We will pick up from here next time.
This lecture was recorded by Theodore P. Pavlic as part of IEE 475 (Simulating Stochastic Systems) at Arizona State University.
