🎲 Random Number Generator
Generate random numbers in any range - single or multiple, with no duplicates, sorted, or as decimals. Roll any dice from D4 to D100, flip a coin, pick a random item from your list, or generate lottery numbers. Every result is statistically unbiased and generated instantly in your browser.
🎲 Number Generator Settings
📜 Generation History
No history yet. Generate some numbers!
🎯 Roll the Dice
Click any die or button to roll. Supports D4, D6, D8, D10, D12, D20, D100.
⚡ Quick Roll Presets
🪙 Flip a Coin
or press Space
📋 Random Item Picker
Enter items one per line. The picker will randomly select from your list.
⚡ Quick List Templates
📐 How Random Numbers Work
Basic Random Integer
JavaScript's Math.random() returns a float between 0 (inclusive) and 1 (exclusive). We scale it to any range.
Random Integer (min to max):
Math.floor(Math.random() × (max − min + 1)) + min
Random Decimal
Rounded to N places: parseFloat(num.toFixed(N))
Unique Numbers (No Duplicates)
To generate unique numbers, we use a Fisher-Yates shuffle on a range array and take the first N elements.
2. Fisher-Yates Shuffle:
for i from n-1 downto 1:
j = random integer from 0 to i
swap array[i] and array[j]
3. Return first N elements
Coin Flip
Probability: exactly 50% for each outcome
Dice Roll (D-N)
Range: always 1 to N (inclusive)
Is Math.random() Truly Random?
Math.random() is a pseudo-random number generator (PRNG) - it uses an algorithm (typically xorshift128+) seeded by the current time. For cryptographic purposes, use crypto.getRandomValues() instead.
const arr = new Uint32Array(1);
crypto.getRandomValues(arr);
const random = arr[0] / 0xFFFFFFFF;
❓ Frequently Asked Questions
Random Number Generator - How It Works and What Each Mode Is Best For
Random number generation is used everywhere from scientific simulations to game development, lottery draws to security tokens, classroom exercises to decision-making. The right mode depends on what you need - this generator covers the most common use cases with tools designed for each one.
Use Cases - Which Mode to Use
Number Generator
- Single random number: Giveaway winner by entry number, quick decision, temperature simulation
- Multiple numbers, no duplicates: Lottery, raffle, random seating or team assignment
- Multiple numbers with duplicates: Monte Carlo simulation, bootstrapping, random sampling with replacement
- Decimal numbers: Probability experiments, simulation parameters, random coordinates
- Large ranges: Unique ID generation, random file naming
Dice, Coins & Lists
- D6: Standard board games, Monopoly, Yahtzee, Catan
- D20: Dungeons & Dragons attack rolls, skill checks, saving throws
- D4/D8/D10/D12: RPG damage rolls and ability checks
- D100: Percentile rolls, random encounter tables
- Coin flip: Binary decisions, tiebreakers, probability demonstrations
- List picker: Random task assignment, restaurant selection, team pairing
PRNG vs True Random - What's Actually Happening
Most digital random number generators are pseudo-random (PRNG) - they use a deterministic algorithm seeded by unpredictable system data (current time in microseconds, mouse position, hardware noise). The result appears random and passes all statistical tests for randomness, but is technically reproducible if you know the seed.
For everyday uses - picking a winner, rolling dice, generating lottery numbers - PRNG is statistically perfect. Each number has exactly equal probability, and past results have no influence on future ones (they are independent). The gambler's fallacy (thinking a coin is "due" for heads after many tails) doesn't apply - each flip is always 50/50 regardless of history.
For security-critical applications (generating cryptographic keys, tokens, session IDs), this tool uses window.crypto.getRandomValues() - the browser's cryptographically secure random number generator, seeded from hardware entropy. This is the same source used by secure applications.
The Fisher-Yates Shuffle - How "No Duplicates" Works
When you generate multiple unique numbers, the tool uses the Fisher-Yates shuffle algorithm (also called the Knuth shuffle). The method:
- Create an array containing all integers in your range (e.g., 1 to 49)
- Starting from the last element, swap it with a randomly chosen element from the remaining unshuffled portion
- Move to the previous position and repeat until the first element
- Take the first N elements as your result
This produces a perfectly uniform shuffle - every possible ordering has equal probability, and no number can appear twice. It's the standard algorithm used in card shuffling software, lottery systems, and any application requiring truly fair random selection without replacement.