This page collects interactive statistics demos that I have created for teaching, outreach, and self-guided learning.
The goal of these demos is to make statistical ideas easier to see, manipulate, and discuss. Most of them are designed to run directly in the browser, with no login required. They are intended as conceptual companions to lectures, problem sets, and formal derivations, rather than as replacements for mathematical arguments.
I will keep adding new demos over time, covering topics at different levels of statistical learning.
How to use these demos
Each demo is designed around one main statistical idea. Some are suitable for a first exposure to statistics, while others are intended for students who have already seen probability, inference, regression, or statistical computing.
The suggested levels below are approximate. Instructors may adapt the demos for different audiences by changing the discussion questions, notation, and amount of mathematical detail.
Demo catalog
| Demo | Main idea | Recommended level | Format |
|---|---|---|---|
| Law of Large Numbers | Repeated sampling, sample averages, convergence to a population mean | Introductory undergraduate, general audience | Interactive browser demo |
Featured demo
Law of Large Numbers
This demo illustrates how sample averages stabilize as the sample size increases. Students can run repeated simulations, change the sample size, and observe how the empirical average moves toward the population mean.
Useful for: introducing repeated sampling, randomness, simulation, and the distinction between a single random outcome and a long-run pattern.
Suggested audience: introductory statistics, probability review, outreach, or the first week of a quantitative methods course.
Possible classroom prompts:
- What changes when the sample size increases?
- Does the sample average always move closer to the population mean?
- How is long-run stability different from certainty in any one experiment?
- What does the demo show that a formula alone may not make visually obvious?
Topics I plan to add
Future demos may include:
- Sampling variability
- Central limit theorem
- Confidence interval coverage
- Bootstrap resampling
- p-values and repeated sampling
- Regression leverage and influence
- Bias and variance
- Bayesian updating
- Multiple testing
- Randomization inference
Notes
These demos are created and maintained by me. They are designed for teaching and conceptual exploration. Unless otherwise noted, the demos use simulated or publicly available examples and do not collect student data.
Some demos may be revised over time as I use them in teaching and receive feedback from students and colleagues.