Linked from

The 17 pages that link to Random number generation, each with the reason it gives.

• Monte Carlo methodRelated: Sampling algorithms depend on generated values that represent the desired randomness.

• CryptographyRelated: Weak randomness can expose keys even when the cryptographic algorithm is sound.

• Public-key cryptographyRelated: Weak randomness can expose private keys or undermine cryptographic protocols.

• Computer simulationRelated: Stochastic simulations depend on generated samples to represent chance.

• DiceRelated: Dice provide a physical way to generate random values.

• Gambler’s fallacyRelated: Understanding how random sequences are generated helps distinguish chance streaks from patterned outcomes.

• One-time padRelated: Key generation must supply independent, uniformly random symbols for perfect secrecy.

• Discrete-event simulationRelated: Random inputs represent uncertain arrivals, service times, failures, and other durations.

• SpeedrunningRelated: Random outcomes can make otherwise identical attempts differ in speed or difficulty.

• Computer artRelated: Random values introduced controlled variation into otherwise rule-based compositions.

• Discrete uniform distributionRelated: Uniform integer generators aim to select each value in a finite range with equal probability.

• The Art of Computer ProgrammingRelated: Knuth’s analysis of generators informs simulation and reproducible computation.

• Game of chanceRelated: Digital games use it to replace physical dice, shuffled cards, and wheels.

• Continuous uniform distributionRelated: Many generators produce uniform values before transforming them into other distributions.

• Magic 8 BallRelated: Its apparent randomness invites comparisons with simple physical randomizers, despite the toy's uneven probabilities.

• Wheel of FortuneRelated: A wheel can serve as a physical method for selecting a random value.

• Mega MillionsRelated: Random selection is the basis for determining winning numbers.