Utilities

Random Number Generator

Generate random whole numbers between any minimum and maximum, with or without repeats. Runs in your browser.

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How it works

Numbers are generated in your browser with its secure random generator.

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Most tools run directly in your browser. Your entered values are not sent to this Worker.

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About the Random Number Generator

Generate random whole numbers between a minimum and maximum, with or without repeats. Numbers are created in your browser using its secure random generator.

How to use it

Enter the minimum, the maximum and how many numbers you need. Choose Unique if you do not want repeats.

Formula

Each number is picked uniformly between your minimum and maximum, inclusive

Worked example

Pick one number from 1 to 100, or draw 6 unique numbers for a quick selection.

Understanding your result

The numbers are shown in a list that you can copy. Each value is equally likely, because the tool uses your browser's secure random generator.

When it is useful

Common mistake

Asking for more unique numbers than the range contains, for example 10 unique numbers between 1 and 5.

Fair picks, made properly

"Give me a number between 1 and 100" hides two questions: what distribution, and how strong is the randomness? This page answers the first with plain uniformity — every integer in the range is exactly as likely as every other — and the second with your browser's cryptographic generator (crypto.getRandomValues), the same device-grade entropy that secures TLS sessions. Each value is drawn fresh, and the tool's rejection loop eliminates the modulo bias that plagues naive random pickers: it computes the largest multiple of the range that fits the generator's output space and redraws any draw that falls above it, so no value in your range ever gets a slightly better chance than the rest.

Repeats are a policy decision, not an accident. A dice roll wants repeats (two sixes in a row must remain possible); a lottery draw must not (once 7 is out, it cannot come back). The "unique" switch flips the tool accordingly, refusing to produce more uniques than the range actually contains.

A worked example, step by step

Scenario: a classroom needs a fair draw — 6 students from a list of 49, each student exactly as likely as every other, no duplicates.

  1. Minimum 1, maximum 49, count 6, unique: yes. Press Generate.
  2. Each draw reduces to: pick uniformly from [1, 49] using the generator's 32-bit output; discard any draw that the rejection loop rejects (the biased tail above the largest 49-multiple that fits).
  3. Uniqueness: the tool keeps a set and redraws any already-seen value, so the printed six are distinct by construction.
  4. Fairness note: skipping the rejection step would give the first 8 values of every 2³² block a slightly higher chance — invisible in one draw, measurable over a term.
  5. Reuse: press Generate again for the next draw; nothing is stored server-side.

The same flow runs a raffle (1–500, one winner), a dice game (1–6, repeats allowed), a shuffle of seats (1–30 unique), or a spot-check sample from a customer list — one UI, one fairness guarantee, no reload tricks.

What the details line tells you

Under the result, the tool restates exactly what was generated — count, minimum, maximum — so a screenshot is self-describing weeks later. Two boundaries the panel enforces: the count is capped at 100 values per press (for readability, and to keep the page responsive), and a unique draw larger than the range errors out with a plain-English message rather than looping forever. A "raffle of 40 winners among 35 tickets" is not a raffle problem; it is the error message's problem, and it gets named as such.

Where uniform integer draws show up

Classroom picks and giveaway draws are the classic request, and the reasons to prefer a browser generator over names-in-a-hat are both fairness (the rejection loop) and auditability (the result line restates the parameters). Sample selection for quality checks asks for k of n unique values. Game masters rolling dice across a table get a shared result everyone can see. Quiz-set randomisation picks question numbers for a quiz with fixed size. In each case the pattern is: define range and count, decide the repeats question deliberately, and let the crypto loop do the rest.

Mistakes that quietly bias a draw

  • Rolling your own modulo. value % n over a 32-bit generator is subtly unfair for almost every n; the tool's rejection loop exists precisely to kill that bias.
  • Using adjacent presses for multiple winners. Each press is independent; a single press with count = winners is the one-shot way to keep odds clean.
  • Treating "unique" as a second draw with the value removed. The tool does it properly in one pass; hand-simulations get it wrong at the boundary (last slot).
  • Confusing uniform with "no repeats possible". Repeats are the honest default for dice-like requests; unique is a mode you opt into on purpose.
  • Recycling a screenshot as a fresh draw. The same numbers printed twice are the same draw; generate again for each new decision.

Keep exploring

Frequently asked questions

Is it truly random?

It uses your browser's cryptographic random generator, which is suitable for everyday draws and games.

How do I avoid duplicate numbers?

Set Unique numbers to Yes. The count then cannot exceed the size of the range.

How is the draw kept fair?

The browser's cryptographic generator feeds a rejection loop: any draw above the largest multiple of your range is discarded, so no value gets the slight edge that naive modulo methods produce.

When should repeats be allowed?

Dice-style draws need them — two sixes in a row must stay possible. Raffles, seat shuffles and sample picks want unique on instead.

What if I request more uniques than the range holds?

The tool answers with a plain-English error rather than looping: 40 unique winners among 35 tickets is not a drawable request.

Is the result stored anywhere?

No. Values are generated in your browser at press time; a screenshot is the only record, and each new decision deserves a fresh draw.

Related guides

Page reviewed 21 October 2026. Results are estimates for general information. See the methodology and disclaimer.

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Show your working

Every tool lists its formula and a worked example, so you can verify the answer yourself.

Understand the result

Each page explains how to read the result, when to use it and the mistakes to avoid.

Clear limits

Estimates are labelled as estimates. Read how we work on the methodology page.

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