Probability Distribution Simulator
Simulate hundreds of random draws from a uniform, normal, or binomial distribution and watch the resulting shape build in a live histogram.
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How the Probability Distribution Simulator works
Probability distributions describe how likely different outcomes are, and the best way to build real intuition for them is to simulate draws and watch the shape emerge — a flat uniform distribution stays roughly level across bins, a normal distribution piles up around the centre, and a binomial distribution (modelled here as the sum of 20 fair coin flips) also clusters near the middle but through a fundamentally different mechanism.
This is a teaching and exploration tool rather than a data export tool: it renders a live bar-style histogram of your simulated draws so you can directly compare how sample size and distribution choice change the visible shape — larger sample sizes will make the underlying distribution's shape noticeably clearer and less jagged.
How to use it
Frequently asked questions
Why does the histogram look jagged with few draws?
Small samples show more random noise; increase the draw count toward the upper end (several thousand) to see a smoother, more recognisable shape.
How is the binomial distribution simulated?
Each draw sums 20 independent fair coin flips (heads = 1, tails = 0) and normalizes the result, which by the central limit theorem approximates a normal shape at high flip counts.
Can I export the raw values?
This tool is built for visual exploration rather than data export; for raw values to analyse elsewhere, use the Gaussian Generator or Random Number List tools instead.