Random Number Generator
Settings
Uses `crypto.getRandomValues()` for high-quality, unpredictable random numbers. Not reproducible by seed.
Using a seed ensures that the same sequence of numbers is generated every time (reproducibility). This uses the ARC4-based PRNG from seedrandom.js.
Results
Summary Statistics
Distribution Chart
How Random Number Generators Work
A Random Number Generator (RNG) is an algorithm that produces sequences of numbers that should ideally lack any predictable pattern. There are two main types of RNGs used in computing:
- Pseudo-Random Number Generators (PRNGs): These are algorithms that use a mathematical formula to produce a sequence of numbers. They are not truly random because they are deterministic; given a starting value, called a seed, the same sequence will be produced every time. This is useful for simulations and testing where you need to reproduce results. This tool uses a PRNG when the "Use Seed" option is enabled.
- Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs) or True Random Number Generators (TRNGs): These generators are designed to be unpredictable and are suitable for security-sensitive applications. They often gather entropy (randomness) from physical sources in the computer's environment, such as mouse movements, keyboard timing, or system electrical noise. This tool uses your browser's built-in cryptographic generator (`crypto.getRandomValues()`) when the "Use Crypto-Secure RNG" option is checked. These results are not reproducible.
Common Use Cases
- Simulations & Modeling: Simulating random events in science, finance, and engineering, such as modeling particle physics or stock market fluctuations.
- Gaming: Determining random events in games, like dice rolls, card shuffles, or loot drops.
- Cryptography: Generating secure keys, salts, and nonces for encrypting data.
- Statistical Sampling: Selecting random samples from a larger population for surveys and studies.
- Art & Music: Creating generative art or procedural music with random elements.
Limitations & Best Practices
- Reproducibility: For scientific work or debugging, always use a seeded PRNG so your results can be verified and reproduced. Note down the seed and all parameters.
- Security: Never use a simple PRNG (like `Math.random()` or a seeded generator) for cryptographic purposes. Always use a CSPRNG designed for security.
- Distribution Matters: Ensure you choose the correct distribution for your needs. A uniform distribution gives every number an equal chance, while a normal (Gaussian) distribution clusters results around a central mean value.
- Bias Testing: For very high-stakes applications, random number sequences should be run through statistical test suites (like the Diehard tests) to check for non-random patterns or bias.
FAQ
Here are some frequently asked questions about this random number generator.
- What is a random number generator?
- A Random Number Generator (RNG) is a tool or algorithm that creates a sequence of numbers that lack any discernible pattern, appearing to be random. They are used in everything from gaming and simulations to cryptography and statistical sampling.
- What is the difference between a PRNG and a CSPRNG?
- A PRNG (Pseudo-Random Number Generator) is an algorithm that produces a sequence of numbers that appears random but is actually determined by an initial value called a 'seed'. Given the same seed, it will always produce the same sequence. A CSPRNG (Cryptographically Secure PRNG) is designed to be unpredictable and is suitable for security-sensitive applications, drawing entropy from sources like system noise.
- What does 'seeding' a random number generator mean?
- Seeding a random number generator provides it with an initial starting value. For a pseudo-random generator, this makes the output deterministic and reproducible. If you use the same seed, you will get the exact same sequence of 'random' numbers every time, which is useful for testing, simulations, and sharing results.
- What is a Normal (Gaussian) distribution?
- A Normal distribution, often called a bell curve, is a probability distribution that is symmetric about the mean. It shows that data near the mean are more frequent in occurrence than data far from the mean. It's defined by its mean (center of the peak) and standard deviation (how spread out the values are).
- Can this tool generate truly random numbers?
- When the 'Use Crypto-Secure RNG' option is enabled, this tool uses your browser's `crypto.getRandomValues()` API, which is designed to provide cryptographically strong, unpredictable random numbers suitable for most security purposes. When this option is off, it uses a pseudo-random generator, which is not suitable for security but allows for reproducible results using a seed.
- Is my data safe and private?
- Yes. All generation and calculations are performed entirely on your device (client-side) within your browser. No data, settings, or results are ever sent to a server. Your privacy is fully protected.
