In general, simulation is modelling of a process or phenomenon. In statistics, Monte Carlo simulation is often used to model outcomes of a random experiment. This kind of simulation rests on generation of pseudo-random numbers – that is, numbers which behave like truly random numbers, though generated by a deterministic (non-random) algorithm.
For example, one could model the length of a queue at a bank over time by randomly picking a number from 1-10 every 6 seconds. If the number is a “1,” a person joins the queue. Each person stays in the queue for 1 minute. The outcome of interest is the length of the queue, recorded over time.
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