Predictions from modeling and simulation (M&S) are increasingly relied upon to inform critical decision making in a variety of industries including defense and aerospace. As such, it is imperative to understand and quantify the uncertainties associated with the computational models used, the inputs to the models, and the data used for calibration and validation of the models. The rapidly evolving field of uncertainty quantification (UQ) combines elements of statistics, applied mathematics, and discipline engineering to provide this utility for M&S. This mini tutorial provides an introduction to UQ for M&S geared towards engineers and analysts with little-to-no experience in the field but with some knowledge of probability and statistics. A brief review of basic probability will be provided before discussing some core UQ concepts in more detail, including uncertainty propagation and the use of Monte Carlo simulation for making probabilistic predictions with computational models, model calibration to estimate uncertainty in model input parameters using experimental data, and sensitivity analysis for identifying the most important and influential model inputs parameters. Examples from relevant NASA applications are included and references are provided throughout to point viewers to resources for further study.