Generate pseudo random numbers
To generate pseudo random numbers, use a programming language or tool with a pseudorandom number generator, choose the required range, and request each value. Set a seed for repeatable output; use a security-focused generator when values must be unpredictable.
How it works
A pseudorandom number generator is an algorithm that produces a sequence from an internal state. The results can appear irregular even though the same algorithm and starting state reproduce the same sequence. That repeatability is useful in testing, simulations, procedural content, and demonstrations.
The seed establishes or helps establish the initial state. A fixed seed makes a run reproducible, which helps when investigating a bug. A changing seed produces a different sequence in ordinary use, although that alone does not make the generator suitable for security. Random-looking is a visual impression; unpredictable is a security property.
Choose the appropriate tool
Most programming languages include a convenient general-purpose random facility. Command-line environments, spreadsheets, calculators, and database systems may provide similar functions. The best choice is usually the tool already used by the surrounding task, because it avoids unnecessary conversion and integration work.
Use an ordinary generator for games, samples, tests, and simulations. Use a fixed seed when another person must reproduce the result. Use a cryptographically secure generator for tokens, secrets, and security decisions.
Record the range and whether its upper boundary is included. Avoid converting fractional output carelessly when uniform integers matter. Interfaces differ. One tool may generate a fractional value, another may accept minimum and maximum integers, and another may return random bytes. Read the function’s boundary rules before depending on the result.
Ranges, bias, and repeatability
Generating an integer within a range requires more care than merely taking arbitrary output and applying a remainder operation. Some mappings favor particular results when the generator’s possible outputs do not divide evenly across the desired range. Established library functions normally handle this concern more reliably than improvised arithmetic.
For experiments, save enough information to recreate the run: Record the generator or library used. Store the seed when reproducibility is intended. Describe the requested range and boundary convention.
Keep the generation order stable if later values depend on earlier calls. A seed is not a universal recipe across tools. Different algorithms can produce different sequences from the same seed. Software updates may also change undocumented behavior, so important reproducibility should include the environment and generator choice.
Questions and answers
- Are pseudo random numbers truly random?
- They are generated deterministically from algorithmic state, although their distribution can be suitable for many practical tasks.
- Why would I set a seed?
- A fixed seed lets you repeat a sequence, making tests and experiments easier to reproduce.
- Can I use a normal random function for passwords?
- Do not assume so. Use a generator explicitly designed for cryptographic security and the platform’s supported security interface.
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