In this lecture, we re-motivate the topic of Input Modeling in stochastic simulation. Input modeling is the process of choosing probabilistic models to represent realistic variation that is statistically similar to measured data even though the probabilistic models leave out the real-world details underlying that variation. This lecture focus primarily on issues relating to collecting data (when to use old data, issues related to data censoring, issues related to correlated variables, issues related to multi-modal distributions, etc.).