Probability Simulator Guide
The Probability Simulator runs Bernoulli trials at a fixed success probability and visualizes the distribution of success counts across many repeated experiments. It helps you intuitively understand statistical behavior in scenarios like coin flips, gacha pulls, and A/B tests where outcomes depend on chance.
Enter three values: success probability (%), trials per experiment, and number of experiment repetitions. For example, simulating a 30% gacha drawn 100 times, repeated 1000 times, yields an average of 30 successes per run with a measurable standard deviation. The tool shows both theoretical (np, √np(1-p)) and simulated mean/std, so you can compare accuracy at a glance.
By the Law of Large Numbers, results converge to theoretical values as repetitions grow. Use it for game probability validation, statistics learning, and experiment design.
Frequently Asked Questions
Yes. More iterations bring the simulation closer to the theoretical value by the Law of Large Numbers.
An independent trial with only two outcomes—success or failure. Coin flips and gacha pulls are typical examples.
For n trials with probability p, mean is np and variance is np(1-p).