Random Number Generator

What does this Random Number Generator do?

This tool generates one or more random integers within any range you specify, with an optional "no repeats" mode for situations like raffles, team draws, or picking unique winners.

How it works

In standard mode, each number is drawn independently at random within your chosen minimum and maximum, meaning the same number can appear more than once across multiple draws. In "no repeats" mode, the generator builds the full list of possible values, shuffles it using a Fisher-Yates shuffle (a well-known unbiased shuffling algorithm), and returns the first however-many values you asked for — guaranteeing no duplicates.

Step-by-step example

Generating 5 unique numbers between 1 and 100 with "no repeats" enabled: the calculator builds a list of all 100 possible values, shuffles that list fairly, and returns the first 5 — for example, 47, 12, 88, 3, 61 — with no value able to appear twice.

Tips & common mistakes

• Without "no repeats" enabled, it's mathematically possible (and increasingly likely with more draws) to get the same number more than once — this is normal random behavior, not a bug.
• For anything with real stakes (a paid raffle, an official drawing), make sure the number of unique values requested doesn't exceed the size of your range — asking for 10 unique numbers between 1 and 5 isn't possible.

Where randomness comes from in a browser

This generator uses JavaScript's Math.random(), a pseudo-random number generator suitable for everyday, non-security-sensitive randomness like games, sampling, drawings, and simulations. For genuinely security-critical randomness (like generating a password or a cryptographic key), a different, cryptographically secure source is needed — see the Password Generator on this site for that use case specifically.

The Fisher-Yates shuffle, briefly

When "no repeats" mode is enabled, this tool uses the Fisher-Yates shuffle algorithm to randomly reorder the full list of possible values before drawing from the top. This algorithm is specifically designed to produce a genuinely unbiased shuffle — every possible ordering of the list is equally likely — which matters for anything where fairness is a real concern, like a raffle or a randomized team draw, as opposed to naive shuffling approaches that can subtly favor certain outcomes.

More tips

• For a fair raffle or drawing with real stakes, always use "no repeats" mode if you're drawing multiple distinct winners from the same pool — otherwise, the same number could theoretically be drawn twice.
• Random number generation is also widely used outside of games — statisticians use it for random sampling, researchers for randomized controlled trial assignment, and developers for generating test data.

True randomness versus pseudo-randomness

"True" randomness typically comes from physical processes (atmospheric noise, radioactive decay, hardware sensors), while "pseudo-random" generators like this one use deterministic algorithms that produce output statistically indistinguishable from true randomness for practical purposes, without needing specialized hardware. For everyday uses like games, raffles, and sampling, pseudo-random generation is entirely sufficient.

Random sampling in research and statistics

Proper random sampling — where every member of a population has an equal, independent chance of selection — is foundational to valid statistical research and survey methodology. A random number generator is often used as the practical mechanism to select which specific individuals or items from a larger list get included in a sample.

A worked example: simulating a dice roll

To simulate a single six-sided die, set minimum to 1 and maximum to 6, and generate one number — each of the six outcomes has an equal, independent 1-in-6 chance on every roll, exactly matching a fair physical die's probability distribution.

Frequently asked questions