Statistics simulators
Randomness, distributions and the patterns that emerge from them.
Central Limit Theorem
Average samples from any distribution and watch the means pile up into a Gaussian, faster as the sample size grows.
Random Walk
Thousands of aimless walkers spread from a point into a Gaussian whose width grows like the square root of time, the particle view of diffusion.
Joint, Marginals & Conditionals
Every pair of values gets a square whose area is its probability. Drag a box over the table and read the joint, the marginals and a conditional off one selection.
Probability Density
No single value has any probability, only stretches do, so a density is the height whose area is that probability. Drag an interval and read off the integral.
Monte Carlo
Throw random darts at a square with a circle inside and count the hits. The fraction inside estimates π, converging slowly as 1/√n.
Bayes' Theorem
See why a 90% accurate test for a rare disease is usually wrong when it says positive. 1,000 people as dots make the posterior obvious.
Maximum Likelihood
Slide a Gaussian over sample data and watch the likelihood rise and fall. The best fit lands exactly on the sample mean and standard deviation.
Least Squares
Drag data points and fit the best line live. The residuals are drawn as literal squares, least squares minimises their total area.
Wasserstein Distance
Measure the distance between two distributions as the least work to reshape one into the other, the area between their CDFs.