Random Number Generator
Generate random numbers instantly. Set your min, max, and quantity.
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True vs. Pseudo-Random Numbers
Most software uses pseudo-random number generators (PRNGs) — deterministic algorithms seeded with a value. Cryptographically secure random numbers use hardware entropy sources and are unpredictable even if the algorithm is known.
Use Cases by Type
- Simulations & games — PRNGs are sufficient (Math.random())
- Passwords & tokens — require cryptographically secure CSPRNG
- Statistical sampling — hardware entropy preferred
- Lottery & giveaways — use crypto.getRandomValues() for fairness
Probability & Expected Values
- Each number in a range is equally likely (uniform distribution)
- 6-sided die: each face has 1/6 ≈ 16.7% probability
- Lottery 6/49: probability of jackpot = 1 in 13,983,816
- Gambler's fallacy: past results do not influence future random numbers
Is Math.random() secure for passwords?
No — Math.random() is a PRNG seeded from a small entropy source. It can be predicted if an attacker observes enough outputs. For password generation and cryptographic keys, always use crypto.getRandomValues() (browser) or crypto.randomBytes() (Node.js).
How do I generate a random number in JavaScript?
Basic: Math.floor(Math.random() * (max - min + 1)) + min. Cryptographically secure: const arr = new Uint32Array(1); crypto.getRandomValues(arr); const num = (arr[0] / 2**32) * (max - min) + min;. The crypto version uses OS-level entropy.
What distribution should I use for my simulation?
Uniform distribution (equal probability) is the default and suits dice, card draws, and random samples. Normal (Gaussian) distribution suits modeling natural variation (heights, errors). Poisson suits rare events over time (server requests per second). Choose based on your real-world model.