Why randomness underpins digital systems
Random number generators (RNGs) produce unpredictable values for simulations, security, and fairness in processes like shuffling or sampling.
True vs. pseudo randomness
Pseudo-random generators use deterministic algorithms seeded with initial values; they are fast and repeatable.
True RNGs derive entropy from physical processes such as thermal noise or quantum effects when unpredictability is critical.
Applications across fields
Data scientists rely on RNGs for Monte Carlo simulations, A/B tests, and sampling large datasets.
Cryptography depends on strong randomness to create keys, nonces, and tokens resistant to prediction.
Quality matters
Poor seeding can produce patterns that attackers exploit or that bias experiments.
Using browser APIs like crypto.getRandomValues() ensures higher entropy than Math.random() for security-sensitive contexts.
Use cases
- Picking contest winners or raffle numbers
- Distributing load by assigning users to shards
- Generating placeholder datasets for demos
- Selecting unique identifiers when collisions must stay low
Best practices
- Document the seed if you need reproducible sequences
- Fallback to cryptographically secure RNGs for authentication or secrets
- Validate that generated ranges include both endpoints when required
- Log usage carefully to avoid leaking sensitive seeds