What is the outcome of running a sufficiently large set of simulation trials?

• A. It provides enough data to predict how the real system will operate.
• B. It guarantees perfect results.
• C. It eliminates all uncertainty.
• D. It reduces computation time.

Answer: (A)

When you run a large number of simulation trials, the outcomes start to converge toward the true behavior of the real system. This happens because, with many random samples, the estimated statistics (like average performance, probability of a result, and risk measures) stabilize and become more representative of what you would observe in reality. This is the essence of the law of large numbers: more trials yield more reliable estimates, so you can use the data to forecast how the system is likely to operate.

However, this doesn’t guarantee perfection. There will always be some remaining uncertainty from the randomness you’re modeling and from any simplifications or wrong assumptions in the model itself. More trials mainly improve precision, not absolve all ambiguity. Also, conducting more trials increases computation time, rather than reducing it.

So the correct takeaway is that a sufficiently large set of trials provides enough data to predict how the real system will operate, giving you practical, probabilistic insights rather than exact, perfect predictions.