This workshop introduces engineers to the fundamentals and practical applications of probabilistic methods used in reliability assessment and risk-informed engineering decision-making. Participants will learn to quantify uncertainty in material properties, geometry, and loading, and apply statistical modeling and simulation techniques–such as Monte Carlo and Latin Hypercube sampling–to support safer and more robust designs. Drawing on aerospace and defense applications, including fleet-level reliability programs, the course demonstrates how probabilistic methods enhance traditional safety-factor approaches and result in more resilient engineering outcomes. Key topics include sources of uncertainty, probability distributions, reliability modeling, simulation techniques, and applications to mechanical and aerospace systems. A hands-on session guides participants through building a Monte Carlo simulation in Excel or MATLAB/Python to estimate probability of failure and compare deterministic versus probabilistic design approaches.