Random Number Generator

The RNG tool generates random numbers within a defined numeric range for software testing, simulations, analytics, and educational demonstrations. It supports reproducible sequences via a seed and non-deterministic runs when no seed is provided, enabling both controlled experiments and exploratory randomness. Users include software developers, QA engineers, data scientists, game designers, and instructors who need predictable data for tests or realistic variability for simulations. Use cases include unit-test data generation, Monte Carlo experiments, and randomized sampling demonstrations across engineering and research workflows. Core features: required inputs are min_value, max_value, count, distribution; optional seed, mean, and stddev for normal draws. Default distribution is uniform; outputs are an array of numbers plus seed_used and distribution_used for traceability. Optional export supports integration with data pipelines. Algorithms & calculations: Uniform draws rely on a seeded PRNG to produce values in [min, max]. Normal draws use Box-Muller transforms to map pairs of uniform samples to a normal value, scaled by mean and stddev. Validation enforces min < max and count > 0; missing mean/stddev for normal or missing seed behaviors are addressed; very large counts may require streaming. Locale formatting uses dot decimals; downstream systems may localize. Assumes 64-bit floating point arithmetic; not cryptographically secure; excludes external entropy sources and cross-process guarantees.