How to use Random Number Generator
Generate a cryptographically random number within any range you specify — from a simple 1–10 roll to large integers for sampling and simulations. All generation uses the browser's Crypto API (window.crypto.getRandomValues) for true randomness, not a predictable pseudo-random seed. Supports single numbers, ranges, multiple draws, and unique (non-repeating) sets.
- Enter the minimum value (e.g., 1) in the "From" field.
- Enter the maximum value (e.g., 100) in the "To" field.
- Optionally set "How many numbers" for multiple results.
- Toggle "Unique" to prevent duplicate numbers in multi-number draws.
- Click Generate and copy individual results or the full set.
Your data never leaves your device — 100% private processing.
True random vs. pseudo-random numbers
Most programming language random functions (Math.random() in JavaScript, random.random() in Python) use a pseudo-random number generator (PRNG) — a deterministic algorithm seeded with a value like the current timestamp. PRNGs are predictable if the seed is known, making them unsuitable for security purposes. The Web Crypto API's getRandomValues() draws from the OS entropy pool (hardware events, timing jitter) to produce cryptographically secure random numbers — suitable for cryptographic keys, tokens, and any security-sensitive use. This tool exclusively uses the Crypto API.
Use cases by range
Range 1–6 simulates a standard die roll. Range 1–2 or 0–1 is a fair coin flip. Range 1–52 maps to a shuffled deck. Range 0–255 generates a random byte value. Range 1–1,000,000 is useful for lottery-style draws or raffle picks. For statistical sampling, generate unique numbers in a range to select random records from a dataset. For simulation modeling (Monte Carlo methods), generate large batches with specific distributions.
| Use case | Suggested range | Notes |
|---|---|---|
| Die roll (d6) | 1–6 | Standard board game die |
| Coin flip | 0–1 | 0 = tails, 1 = heads |
| Card draw | 1–52 | Map to rank × suit |
| Raffle pick | 1–N | Set N to number of entries |
| Byte value | 0–255 | Useful for color or data generation |
Worked examples
Pick 1–100 uniformly
Inputs: min 1 · max 100
Result: each value ≈1% probability (rejection-sampled, unbiased)
Simulate a d6
Inputs: min 1 · max 6
Result: each face 1/6 ≈ 16.7%
Glossary
- CSPRNG
- Cryptographically Secure Pseudo-Random Number Generator — suitable for security and cryptography applications.
- Entropy pool
- A collection of unpredictable data from hardware events used to seed cryptographic random number generators.
- Monte Carlo method
- A computational technique that uses random sampling to solve problems or estimate probabilities.
- Uniform distribution
- Each value in a range has an equal probability of being selected.
Related reading
Frequently Asked Questions
Why use Random Number Generator?
- Draw from the Web Crypto API for unbiased, security-grade integer generation
- Generate large batches of unique non-repeating integers in a single click
- Set any min/max range from single digits to billions for any sampling need
- Avoid predictable Math.random() seeds that introduce bias in simulations
Common use cases
- Pick a random participant number for a classroom quiz cold-call
- Select a random record index when sampling rows from a large dataset
- Generate a unique raffle ticket number for a prize draw event
- Roll a custom-sided die for a board game that lacks the right physical die
- Produce a random seed value for initializing a reproducible simulation run
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