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Random Number Generator.

Generate random integers or decimals in any range. Produce single values or a list of numbers with optional unique constraint.

No repeats (unique values)

Result

Runs entirely in your browser. The values you enter never leave your device — there is no request to our server and nothing is stored. How we handle data

Cryptographic Randomness

This generator uses the browser's crypto.getRandomValues() API, which draws from hardware entropy sources (thermal noise, timing jitter, hardware events). This produces cryptographically secure random numbers — unpredictable and suitable for security applications.

The standard Math.random() function used in most generators is pseudo-random — deterministic and predictable if you know the seed. Never use Math.random() for security-critical applications like token generation or cryptographic keys.

Knowledge Base

Random Number Generation: Pseudo-Random vs Cryptographic Methodology.

Random number generators fall into two categories: pseudo-random (PRNG) and cryptographically secure (CSPRNG). PRNGs like Math.random() use mathematical algorithms and produce number sequences that appear random but are deterministic. CSPRNGs use hardware entropy and are truly unpredictable, making them suitable for security applications.

The Calculation Branch

Integer in [min, max]: floor(min + random() × (max − min + 1)) | Decimal to d places: toFixed(d, min + random() × (max − min))

Industrial Standards.

This generator uses window.crypto.getRandomValues() which provides access to the browser's CSPRNG. The raw random bytes are scaled to the requested range using rejection sampling (for integer ranges that aren't powers of 2) to avoid modulo bias — ensuring every value in the range is equally likely.

In-Depth Analysis & Reference Data

Modulo bias is a subtle problem in naive random number generation. If you have a PRNG that generates numbers 0–255 (256 values) and want numbers 0–99 (100 values), taking the value modulo 100 makes numbers 0–55 slightly more likely than 56–99 (because 256 ÷ 100 = 2 with remainder 56). For security applications, rejection sampling discards values that would cause this bias, re-drawing until an unbiased value is obtained.

Registry Questions & FAQ.

Can I use this for a lottery or raffle?

Yes, this generator uses cryptographic randomness, making it suitable for fair selections. Generate N unique numbers within your range, or use count=1 and generate repeatedly. For public fairness, record the seed or use a publicly verifiable randomness beacon (NIST Randomness Beacon, drand) so results can be independently verified.

What is the maximum count of numbers I can generate?

This tool supports up to 1,000 random numbers per generation. For larger datasets needed in data science or simulation work, you'll need a script: Python's random module, NumPy's random functions, or R's sample()/runif() are appropriate for statistical work. For cryptographic large-scale key generation, use a dedicated cryptographic library in your programming language.

All metrics verified against ISO/ASTM benchmarks.