How a Random Number Generator works (and the “formula” behind it)

A random number generator (RNG) is a tool that produces values you can’t reasonably predict ahead of time. In everyday life, you use randomness for things like rolling dice, picking a raffle winner, shuffling a playlist, or choosing a random prompt for journaling. On the internet, RNGs show up everywhere: giveaways, classroom activities, game design, A/B testing demos, and “pick a comment number” challenges.

There are two big categories of RNG: pseudo-random (fast algorithms that look random) and cryptographically secure (randomness that comes from stronger system sources). A lot of simple generators use JavaScript’s Math.random(). It’s fine for quick demos, but it’s not designed to resist prediction. That matters when you care about fairness (for example, picking winners) or when you want to avoid patterns.

This page uses crypto.getRandomValues(), which is the browser’s built-in interface to a stronger randomness source. The browser asks the operating system for random bytes, and the OS gathers entropy from many system events (timers, hardware, and other sources). You don’t need to understand all the details to use it — just know it’s the standard approach for “better randomness” on the web.

When people say “give me a random number between 1 and 100,” they usually mean a uniform selection: every number in the range should have the same chance of being chosen. A correct RNG tries to make each outcome equally likely. That’s different from “random-looking,” which can still be biased if done incorrectly.

For integers, the classic formula people learn is: min + floor(random() × (max − min + 1)). Here, random() is a value between 0 and 1. Multiplying by the size of the range and flooring gives an integer step, and adding min shifts it into your desired bounds.

But there’s a catch: if your random source produces a fixed set of states (like a 32-bit integer), simply taking “modulo range” can introduce a tiny bias (some numbers appear slightly more often). This tool avoids that by using a rejection sampling approach for integers. It repeatedly draws 32-bit values until the value falls in a region that maps evenly to your range. That’s the typical way to avoid modulo bias when you care about fairness.

For decimals, you usually want a number in a continuous interval like 0.0–1.0 or 2.5–7.9. The typical idea is: min + u × (max − min), where u is uniform from 0 to 1. This generator creates u by dividing a 32-bit random integer by 2³². Then it scales the result into your range and rounds to the number of decimal places you choose.

One detail: decimals are effectively infinite — so “unique decimals” is not meaningful in the same way as “unique integers.” You could still request unique decimals, but tiny rounding can cause duplicates. That’s why this tool’s “Unique draw” feature is intended mainly for integer ranges (raffles, question numbers, participant IDs, etc.).

When you select Unique draw, the generator keeps a set of values it already produced and continues drawing until it fills your requested count. For integer ranges, uniqueness is only possible if the range has at least as many numbers as the count you want. Example: if you ask for 10 unique integers between 1 and 6, that’s impossible (there are only 6 unique outcomes). The calculator detects that and tells you to widen the range or lower the count.

Crypto-safe randomness is great for fairness, but it does not automatically make your process “auditable.” If you’re running a high-stakes giveaway, you may also want to record the rules, show your range and settings on screen, and publish the output in a transparent way. This tool helps with that by letting you copy, save, and screenshot the output easily.

Real examples you can copy (games, giveaways, classrooms)

You have 250 valid entries numbered 1–250. You want 1 winner and 2 backups. Set Min = 1, Max = 250, Count = 3, Integers, and turn on Unique draw. The output list gives you three distinct numbers — announce the first as the winner and keep the others as backups.

For a standard die roll, use the Dice preset (1–6) and set Count = 1. Want a D20? Set Min = 1, Max = 20. For advantage rolls, set Count = 2 and choose the higher number (your rules may vary).

You have 30 students and you want a random presentation order. Set Min = 1, Max = 30, Count = 30, Integers, Unique draw, and Sort = none (or ascending if you want to print a tidy list). Now you have a random permutation — a different order each time.

You need 10 random values in the interval 0–1 for a quick simulation demo. Set Min = 0, Max = 1, Count = 10, Type = Decimals, Decimal places = 4. The output becomes a clean list like 0.1837, 0.9821, … which you can paste into a spreadsheet.

For TikTok/Reels challenges where you “let fate decide,” use Min = 1, Max = 10, Count = 1. Screenshot the output and post it like: “I’m doing whatever number I get 😭” (Example: #7 = 7 pushups, 7 days of journaling, 7-minute run, 7 random compliments — you decide the rules.)

• Use Copy to paste into Excel/Google Sheets or a giveaway post.
• Use Unique draw to avoid repeats for winners or random assignments.
• Use Sort if you want your output to look clean and “official.”
• Use Save Result if you want a record on your device.

Step-by-step: what happens when you press “Generate”

Here’s the exact flow, in normal human language:

1. We read your settings. The calculator grabs your Min, Max, Count, Type (integer/decimal), whether you want Unique draws, and whether you want the output sorted.
2. We validate your inputs. It checks that Min and Max are real numbers, that Max ≥ Min, that Count is reasonable (this tool caps bulk generation to keep the page fast), and that “Unique” is possible if you asked for it.
3. We generate randomness using the browser’s crypto source. The tool requests random 32-bit values using crypto.getRandomValues(). This is stronger than basic pseudo-random for fairness and unpredictability.
4. We map random bits into your range. For integers we do a bias-resistant mapping into the inclusive range [Min, Max]. For decimals we create a uniform fraction u and scale it into your range.
5. If Unique is on, we avoid repeats. We store each generated number in a Set and keep drawing until we fill the list.
6. We format the output. The list is displayed in the output box, and we prepare a share-friendly summary string.
7. You can copy, save, or share. The page includes built-in buttons for copy/paste and social share.

Everything above happens locally in your browser — no server calls. That’s why it’s fast, private, and simple.

In fairness-sensitive draws, the key point is to avoid subtle bias. If you simply do “random integer modulo range,” some outcomes can become slightly more likely depending on the size of your range. This generator uses a rejection method so each integer is as evenly distributed as possible.

Frequently Asked Questions

• Is this random number generator truly random?

  It uses your browser’s crypto.getRandomValues(), which is the standard way to generate high-quality randomness on the web. For everyday fairness (games, giveaways, education) it’s excellent. For specialized cryptographic key generation, use dedicated security tools and follow best practices.

• Are the minimum and maximum inclusive?

  For integers, yes: the generator produces numbers in the inclusive range [Min, Max]. For decimals, the value is in the continuous interval between Min and Max and then rounded to your chosen decimal places. Due to rounding, you might occasionally see values that visually look very close to the endpoints.

• What does “Unique draw” mean?

  It means the output will not contain duplicates. This is ideal for picking winners or assigning numbers. If the range is too small to provide the count you requested, the tool will tell you to widen the range or reduce the count.

• Why not just use Math.random()?

  Math.random() is fast and “random-looking,” but it’s not designed for unpredictability or fairness-sensitive work. Crypto-safe randomness reduces predictability and helps avoid patterns. It’s a better default for a public generator.

• Can I use this for a lottery or giveaway?

  Yes for the random selection step — but remember: lotteries have legal rules, and giveaways often require clear eligibility rules. This tool helps you generate the numbers fairly; it doesn’t manage the legal side of promotions.

• Does this tool store my results?

  Not automatically. If you press Save Result, the result is stored locally on your device (in your browser storage) so you can reference it later. Nothing is uploaded to a server.

• How many numbers can I generate at once?

  This page is optimized for fast performance, and it caps bulk generation to keep the browser smooth. If you need thousands of values for research, you’ll want a script or spreadsheet approach.