Random Decimal Generator
Random floats with a chosen distribution, decimal places, and optional seed for reproducible runs.
Random Decimal Generator
Generate random decimal numbers with various distributions, precision control, and statistical analysis
Picking a distribution
Uniform — every value in the range is equally likely. Use for sampling ranges, UI jitter, and as a base for other transforms.
Normal (Gaussian) — cluster around a mean. Use for measurement noise, heights/weights-style data, and anything that should look "natural."
Exponential — many small values, few large ones. Common for wait times and failure intervals.
Beta — stuck between 0 and 1. Handy for proportions and rates.
Precision and seeds
- 1–2 decimal places for money-style displays and simple measurements
- 3–6 for most science and engineering work
- More than that only when you actually need it; floats still have limits
- Set a seed when you need the same sequence again (tests, papers, debugging)
These are pseudorandom numbers. Fine for sims and testing. For crypto keys or secrets, use a proper CSPRNG instead.
Frequently Asked Questions
Which distribution should I use?
Uniform for flat sampling, normal for measurement-like noise, exponential for times between events, beta for values locked between 0 and 1.
How many decimal places?
Match the real precision of your domain. Extra digits look precise but often just amplify floating-point noise.
How do I check the output looks right?
Compare sample mean/variance to theory, glance at a histogram, and watch for weird clumping. For serious work, run a goodness-of-fit test.
Why use a seed?
So you can replay the exact sequence. Useful for unit tests, demos, and reproducing a bug.
Do floating-point limits matter?
Yes, especially with many decimal places or values near extremes. Don't compare floats with ===; for money, prefer decimal arithmetic.
Is this truly random?
No — it's pseudorandom. That's enough for most sims. Not for cryptographic secrets.
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