Random Number Generator

How does the random number generator work?

Our random number generator uses a cryptographically secure algorithm to generate truly random numbers within your specified range. It uses JavaScript's Math.random() function combined with additional entropy sources.

The algorithm works by: (1) Initializing with multiple entropy sources, (2) Applying linear congruential generation with optimized parameters, (3) Performing modular arithmetic to fit your specified range, (4) Ensuring uniform distribution across all possible values.

Can I generate multiple random numbers at once?

Yes! You can specify how many random numbers you want to generate (up to 100 at once). You can also choose whether to allow duplicate numbers or ensure all numbers are unique.

For unique number generation, we use a Fisher-Yates shuffle algorithm variant that ensures each number appears only once while maintaining true randomness in the selection process.

Is this random number generator truly random?

Our generator produces pseudorandom numbers that pass standard statistical tests for randomness and are suitable for most applications including games, research, and decision-making. While not true random (which requires physical processes), the quality is excellent.

For applications requiring true randomness (cryptography, security), we recommend using hardware-based random number generators or specialized cryptographic libraries that access your system's entropy pool.

What's the maximum range I can use?

You can generate random numbers between 0 and 999,999,999. This range covers most practical use cases from simple dice rolls to complex statistical sampling.

The upper limit is set to ensure optimal performance and avoid floating-point precision issues. For applications requiring larger ranges, consider using multiple smaller ranges or specialized big-number libraries.

How can I verify the randomness quality?

You can test randomness by generating large samples (1000+ numbers) and checking for: (1) Uniform distribution across the range, (2) No obvious patterns or sequences, (3) Equal frequency of all numbers over time, (4) Independence between consecutive numbers. Professional statistical software can perform chi-square and other randomness tests.

Can I use this for academic research?

Yes, our random number generator is suitable for educational and academic research purposes. However, for publication-quality research, consider documenting the random number generation method and potentially using established statistical software packages (R, Python's NumPy, MATLAB) that provide detailed documentation and reproducibility features.

What about reproducibility for experiments?

For reproducible experiments, you'll need a seeded random number generator. While our web-based tool doesn't support custom seeds, you can achieve reproducibility by recording all generated numbers and using them consistently, or by implementing seeded generators in statistical software for formal research.