Random Number Generator

Random Numbers Guide

True vs Pseudo-Random

Computer-generated random numbers are technically 'pseudo-random' — they use deterministic algorithms seeded by values like the current time. For almost all practical purposes (games, raffles, statistical sampling, passwords), this is indistinguishable from true randomness. True hardware random number generators use physical processes (radioactive decay, electronic noise) as entropy sources. Web browsers' Math.random() function is cryptographically sufficient for most purposes but should not be

Raffle and Draw Uses

For a raffle with 100 tickets, generate a random integer between 1 and 100. For multi-winner draws, use 'unique' mode to generate multiple non-repeating numbers. For team selection from a list of 20 people, generate numbers 1–20 without repeats and assign names to positions. For a coin flip: 1 = heads, 2 = tails (generate 1 or 2). For a dice roll: 1–6. For two dice: generate two numbers 1–6 separately.

Statistical Sampling

Random sampling is fundamental to statistics — selecting a representative subset without bias. To randomly sample 10 items from a list of 50: generate 10 unique numbers between 1 and 50, then select those items. Stratified random sampling divides the population into groups (strata) and samples randomly within each group — ensuring representation of all subgroups. This is how opinion polls, clinical trials, and quality control sampling are designed.

Random Numbers in Everyday Life

Random number generation underpins: lottery draws (certified hardware RNG), gambling (casino games use hardware RNG for fairness certification), cryptography (encryption keys require high-entropy randomness), computer games (procedural generation of levels, enemies, loot), scientific simulations (Monte Carlo methods), and A/B testing (random assignment of users to test groups). The quality of randomness matters enormously in security contexts — weak randomness in key generation has caused catast