Dice Roll Simulator
Roll virtual dice with 4, 6, 8, 10, 12, or 20 sides, combine multiple dice, and review totals, averages, and distributions.
Random Number Generator
Generate random numbers within a custom range, choose integers or decimals, and control count for testing, sampling, raffles, or classroom use.
Common Random Ranges
| Name | Min | Max | Typical Use |
|---|---|---|---|
| Coin Flip | 0 | 1 | Heads/tails simulation |
| D6 Die | 1 | 6 | Standard board game die |
| D20 Die | 1 | 20 | Role-playing games |
| Playing Card | 1 | 52 | Card position in deck |
| Lottery | 1 | 49 | Common lottery (6/49) |
| Percentage | 0 | 100 | Probability testing |
| Byte value | 0 | 255 | Single computer byte |
| Color channel | 0 | 255 | RGB color component |
| Year (recent) | 1990 | 2025 | Random modern year |
Planning notes, formulas, and examples
About the Random Number Generator
The Random Number Generator creates random numbers within a specified range. Set the minimum, maximum, how many numbers to generate, and whether you want integers or decimals.
Random numbers are used in simulations, games, statistics (random sampling), security (token generation), and decision-making (raffles, lotteries). This calculator uses JavaScript's built-in Math.random() function.
Generate a single number or a batch of up to 100 numbers at once. Results are displayed clearly and can be easily copied for use in spreadsheets, programs, or other applications.
When This Page Helps
Need a quick random number for a game, raffle, stats sampling, or testing? This generator provides exactly the range and quantity you need without writing code.
How to Use the Inputs
- Set the minimum value.
- Set the maximum value.
- Choose how many numbers to generate.
- Select integer or decimal output.
- Click generate and view results.
Formula used
Integer: floor(random() × (max − min + 1)) + min
Decimal: random() × (max − min) + minExample Calculation
Result: e.g. 42, 87, 13, 65, 29
Five random integers between 1 and 100. Each number has an equal probability of being selected.
Tips & Best Practices
- Math.random() is pseudo-random — fine for games, not for cryptography.
- For cryptographically secure random numbers, use crypto.getRandomValues().
- Set min and max to the same value to always get that number (useful for testing).
- Use count > 1 to generate batches for simulations.
- Random decimals provide continuous values; integers provide discrete values.
- Use for lottery numbers, random sampling, or generating test data.
Pseudo-Random vs True Random
PRNGs like Math.random() use algorithms to produce sequences that pass statistical tests for randomness. True random generators (TRNGs) use physical phenomena like thermal noise or radioactive decay.
Applications
Monte Carlo simulations use millions of random numbers to model complex systems. A/B testing randomly assigns users to groups. Games use random numbers for loot drops and map generation.
Uniform Distribution
This generator produces uniformly distributed numbers: every value in the range is equally likely. Other distributions (normal, exponential) require additional transformation.
Mastering this concept provides a strong foundation for advanced coursework in mathematics, statistics, and related quantitative disciplines.
Sources & Methodology
Last updated:
Frequently Asked Questions
No, they are pseudo-random. Math.random() uses an algorithm (PRNG) that produces numbers that appear random but are deterministic. For most purposes, this is sufficient.
For informal purposes, yes. For official lotteries or security-critical applications, use a cryptographically secure random number generator (CSPRNG).
Math.random() returns a decimal ≥ 0 and < 1. This calculator scales and shifts the output to match your specified min and max range.
Yes, unless you explicitly require unique numbers. With random generation, repeats are possible and become more likely as the count approaches the range size.
In Excel or Google Sheets, use =RANDBETWEEN(min, max) for integers or =RAND()*(max-min)+min for decimals. Running this calculation with a range of plausible inputs can help you understand the sensitivity of the result and plan for different scenarios.
A seed is the initial value fed to the algorithm. The same seed always produces the same sequence, which is useful for reproducible simulations.
More in this topic
Coin Flip Simulator
Flip a virtual coin one or multiple times and track heads-versus-tails counts and percentages for quick experiments or games.