Selected Simulation Demonstrations for Introductory Statistics Using R and Python
A hands-on guide to a simulation for those who want to take a first step (9 pages)
Probability simulation is a method of approximating the likelihood of an event by repeating a random experiment many times and calculating the frequency of the desired outcome. It is particularly useful for solving problems where mathematical formulas are difficult to derive. A standard probability simulation follows these steps:
Define the Experiment: Identify the random process (e.g., flipping a coin, rolling dice).
Generate Random Outcomes: Use a computer to “run” the experiment thousands of times.
Evaluate Success: For each run, check if the specific event of interest occurred.
Calculate Probability: Divide the number of successful events by the total number of trials.
Simulation using R or Python, which are designed for statistical computing, makes it easy to run large simulations with minimal code.
Prof. Dr. Yoon G. Kim — California State Polytechnic University, Humboldt
Yoon G. Kim is a Professor of Statistics in the Department of Mathematics at California State Polytechnic University, Humboldt, where he teaches statistics, data analysis, and quantitative methods across undergraduate and graduate programs. His research spans safety engineering, quality assurance, industrial engineering, and applied statistics. A dedicated educator and practitioner, he brings rigorous quantitative expertise and a passion for evidence-based inquiry to his teaching, research, and professional contributions — including his most recent work on redwood ecosystem restoration in northern California.