Random Number Generator

Generate random integers, decimals, or sequences. Create lottery numbers, shuffle lists, or get multiple random values with or without duplicates.

Formula:Math.random()

Generated Numbers

Random Number

42

Generator Mode

Settings

Results

42

Quick Presets

Generated Numbers

Random Number

42

?How Do You Generate Random Numbers?

A random number generator produces unpredictable numbers within a specified range. Computers use pseudo-random algorithms that appear random but are deterministic. For true randomness, hardware random generators use physical phenomena. Common uses: games, simulations, cryptography, statistical sampling, and lottery/raffle selections.

What is a Random Number Generator?

A random number generator (RNG) is a system that produces numbers that lack any pattern or predictability. Computer-based RNGs typically use pseudo-random algorithms seeded with varying inputs to produce sequences that statistically appear random. RNGs are essential for games, cryptography, simulations, statistical sampling, and many decision-making applications.

Key Facts About Random Numbers

  • Pseudo-random generators use algorithms; results are deterministic but appear random
  • Set min and max values to define the range of possible numbers
  • Uniform distribution: all numbers equally likely
  • Can generate integers only or include decimals
  • Useful for: games, simulations, sampling, lottery picks, decision making
  • Dice roll simulation: 1-6 for standard die, sum of multiple dice
  • Random sequences can include or exclude duplicates
  • Seed values allow reproducing the same random sequence

Quick Answer

A random number generator produces unpredictable numbers within a specified range. Computers use pseudo-random algorithms that appear random but are deterministic. For true randomness, hardware random generators use physical phenomena. Common uses: games, simulations, cryptography, statistical sampling, and lottery/raffle selections.

Frequently Asked Questions

This tool uses JavaScript's Math.random() function, which generates pseudo-random numbers using algorithms like the Mersenne Twister. While not truly random (they're deterministic), they're sufficient for most applications except cryptography.
Pseudo-random numbers are generated by algorithms that produce sequences appearing random but are actually deterministic. True random numbers come from physical phenomena like atmospheric noise. Pseudo-random is faster and usually sufficient.
Use no duplicates when each number should appear only once, like lottery picks, raffle drawings, or shuffling cards. Note that the range (max - min + 1) must be at least as large as the count of numbers requested.
The Fisher-Yates (or Knuth) shuffle is an algorithm that produces an unbiased random permutation of a sequence. It iterates through the array from end to start, swapping each element with a randomly chosen element from the remaining unshuffled portion.
Use "Sequence (No Duplicates)" mode. Set min to 1, max to the highest lottery number (e.g., 49 or 69), and count to how many numbers you need (e.g., 6). This ensures no repeated numbers, matching lottery rules.
No. These numbers are generated client-side using Math.random(), which is not cryptographically secure. For passwords, use a dedicated password generator or the Web Crypto API with crypto.getRandomValues().

Last updated: 2025-01-15