Random Number Generator

True random vs pseudo-random

Most random number generators in programming use pseudo-random algorithms (like Math.random()) that produce deterministic sequences from a seed. This tool uses crypto.getRandomValues() — a cryptographically secure source based on hardware entropy — making the output genuinely unpredictable and suitable for security-sensitive applications.

Use cases for random numbers

• Lottery / raffles: Generate unique numbers in a range with no duplicates.
• Games and simulations: Dice rolls, card draws, RPG encounters.
• Random sampling: Pick N items from a numbered list.
• Testing and development: Generate test data within specific ranges.
• Statistics: Generate random samples for calculations.

The 'no duplicates' algorithm

For integer ranges, the no-duplicates mode uses a Fisher-Yates shuffle on the full range array, then takes the first N values. This guarantees true uniform distribution with no repetition. For decimal numbers, duplicates are exceedingly rare given the precision of floating-point arithmetic.