Simulates standardized sums Zₙ of many i.i.d. random variables from different base distributions (uniform, ±1, shifted exponential) and shows how the histogram of Zₙ approaches the overlaid standard normal curve N(0,1) as n increases. Side panels emphasize the CLT conditions (independence, finite variance) and the standardization step (centering by nμ and scaling by σ√n).
Per-frame Monte Carlo sampling updates a 64-bin histogram over z∈[-4,4] with exponential forgetting and light 1D smoothing. The visualization cycles through n values (1→100) and base distributions, overlaying the analytic N(0,1) density for comparison. All drawing is grid-snapped for a blocky aesthetic and uses only the Canvas 2D API.