Quick Reference: Dice Probabilities
| Dice | Sides | Min Roll | Max Roll (2 dice) |
|---|---|---|---|
| D6 (standard) | 6 | 1 | 12 |
| D20 (RPG) | 20 | 1 | 40 |
| D10 | 10 | 1 | 20 |
| D100 (percentile) | 100 | 1 | 200 |
Random Number Generator
Generate random numbers instantly. Choose a range, pick how many numbers, and optionally ensure no duplicates. Great for lotteries, games, giveaways, random selection, and statistical sampling.
Random Number Generator
Frequently Asked Questions
Are these numbers truly random?
This tool uses JavaScript's Math.random() which is a pseudo-random number generator (PRNG). It produces statistically random results suitable for games, giveaways, and sampling — but not for cryptographic security purposes.
How do I pick a random winner?
Assign each participant a number (e.g., 1 through 50), then use the Single Number mode with your min and max to pick a random winner. For multiple winners, use the List mode with unique numbers enabled.
Inclusive ranges
A range of 1 to 10 includes both endpoints, giving ten possible outcomes each with a 10% chance. This matters when generating array indices or simulating dice — a range of 0 to 5 is six outcomes, not five.
With and without repeats
Allowing repeats draws each number independently, so the same value can appear more than once. This models rolling a die repeatedly.
Disallowing repeats removes each number once drawn, which models dealing cards or a raffle. It also means you cannot request more numbers than the range contains — you cannot draw 15 unique values from 1 to 10.
Pseudorandom, and why that's fine
Computers generate numbers using deterministic algorithms seeded from system state. The output is not random in a philosophical sense — given the same seed, the same sequence follows.
In practice the distribution is uniform and the sequence unpredictable without the seed, which is entirely adequate for games, sampling, simulations and picking winners.
Where it is not adequate is cryptography. Generating keys or security tokens requires a cryptographically secure generator, which draws on genuinely unpredictable system entropy. A standard generator can be predicted by an attacker who can observe enough output.
Interpreting results
Small samples look lumpy. Ten rolls of a die producing three sixes and no fours is unremarkable, not evidence of bias. Uniformity emerges over large numbers of trials, not small ones.
Expecting short runs to look evenly spread is the same intuition error behind the gambler's fallacy — genuine randomness clusters more than people anticipate.