What Is a Random Number Generator?
A random number generator (RNG) is a tool that produces numbers that lack any predictable pattern. Our generator uses cryptographically secure randomness provided by the .NET RandomNumberGenerator class, ensuring that every number generated is truly unpredictable and uniformly distributed within the specified range.
Unlike simple pseudo-random number generators (PRNGs) that use mathematical formulas and can be predicted if the seed is known, a cryptographic RNG draws entropy from the operating system's secure random source, making the output suitable for security-sensitive applications.
Key fact: This tool supports any integer range from extremely large negative numbers to extremely large positive numbers. The random values are generated server-side using cryptographically secure methods, ensuring true randomness and uniform distribution.
How Does Random Number Generation Work?
- Entropy collection — The operating system gathers randomness from hardware events such as mouse movements, keyboard timings, disk I/O, and dedicated hardware random number generators.
- Secure byte generation — The cryptographic RNG uses this entropy to produce uniformly distributed random bytes.
- Range mapping — The random bytes are converted into a number within the specified min-max range using modular arithmetic, ensuring every value in the range has an equal probability of being selected.
Common Uses for Random Numbers
- Games and lotteries — Random number selection for fair draws, dice rolls, card shuffles, and game mechanics.
- Statistical sampling — Selecting random samples from a population for surveys, experiments, and data analysis.
- Decision making — Randomly choosing between options, assigning tasks, or breaking ties impartially.
- Cryptography — Generating nonces, initialization vectors, and random keys for encryption protocols.
- Testing and QA — Creating random test data, fuzz testing inputs, and randomized test execution order.
- Simulations — Monte Carlo simulations, random walk models, and stochastic processes in science and finance.
- Raffles and giveaways — Picking random winners from a numbered list of participants.
Types of Random Number Generators
- True Random Number Generators (TRNG) — Use physical phenomena (radioactive decay, thermal noise, atmospheric noise) to produce randomness. These are the gold standard but can be slow.
- Pseudo-Random Number Generators (PRNG) — Use deterministic algorithms seeded with an initial value. Fast but predictable if the seed and algorithm are known.
- Cryptographically Secure PRNGs (CSPRNG) — A class of PRNGs designed to be computationally infeasible to predict. This is what our tool uses.
Important: For applications where fairness or security is critical (such as lotteries, cryptographic keys, or scientific research), always use a cryptographically secure random number generator like this one. Standard Math.random() or System.Random are not suitable for such purposes.
Frequently Asked Questions
Is the generated number truly random?
Yes. Our generator uses .NET's RandomNumberGenerator, which is a cryptographically secure random number generator. It draws entropy from the operating system's secure random source, producing numbers that are computationally indistinguishable from true randomness.
Can I generate negative numbers?
Absolutely. Simply enter a negative value in the "From" or "To" field. For example, set From to -100 and To to 100 to generate a random number between -100 and 100, inclusive.
Are both the min and max values inclusive?
Yes. Both the minimum and maximum values are inclusive. If you set the range to 1–10, any integer from 1 to 10 (including both 1 and 10) can be generated.
What happens if min is greater than max?
The tool automatically swaps the values so the smaller number becomes the minimum and the larger becomes the maximum. You will still get a valid random number within the correct range.