Random Number Generator

With duplicates: each number is drawn independently, so the same value can appear more than once (like repeated dice rolls). Without duplicates: like drawing names from a hat — once a number is picked, it can't be picked again. Without-duplicates needs the range to be at least as large as the count.

Most natural measurements (heights, IQ scores, measurement errors) follow a bell curve — most values cluster near the mean and extremes are rare. Setting mean = 0 and SD = 1 gives the standard normal distribution. About 68% of values fall within ±1 SD, 95% within ±2 SD, 99.7% within ±3 SD. Useful for Monte Carlo simulation, statistical testing, and modelling.

Each value gets a relative weight. Higher weight = more likely. The probability of value v is weight(v) ÷ sum(weights). Example: red:3, blue:2, green:5 → total 10, so red 30%, blue 20%, green 50%. Use this for biased dice, loaded lotteries, or sampling with non-uniform probabilities.

Yes. Min can be negative, max can be negative. As long as max > min the calculator handles any range. For Gaussian, the mean can be any number including negatives.

A seed makes randomness reproducible. Same seed + same inputs = same output every time. Useful for debugging, sharing results, or A/B testing. Leave empty for genuinely fresh randomness each click.