Random draws with rules
Fair raffles require uniform probability across eligible entries. Advanced controls—ranges, batch size, uniqueness—keep classroom raffles and QA fuzzing honest about the rules.
Simulation use
Monte Carlo experiments rerun thousands of draws. Seeded runs let you compare code changes without regenerating entirely new noise each time.
Bias pitfalls
Modulo shortcuts on raw RNG output can skew results when the range does not divide the output space evenly. Use rejection sampling or library helpers for uniform ranges.
Compliance
Regulated lotteries need auditable hardware RNG and chain-of-custody—not a web page. Treat public generators as convenience tools, not legal gaming infrastructure.