Which statement best describes the goal of simulation output analysis?

• A. To optimize the code.
• B. To regenerate data.
• C. To summarize results and understand uncertainty.
• D. To maximize speed.

Answer: (C)

The main idea is to summarize the results of many random outcomes and to understand the uncertainty around them. In a Monte Carlo run, each replication can produce a different value, so you don’t rely on a single number. Instead, you look at the aggregated results across replications: the average outcome, the variability, and the full distribution. You quantify uncertainty with measures like confidence intervals or standard errors, and you check whether the results have converged as you increase replications. This helps you make informed decisions under risk, since you can see not just a typical result but also how wide the possible outcomes are and how likely they are.

The other tasks—improving the code, regenerating data, or simply running things faster—aren’t about interpreting results or understanding uncertainty, which is why they aren’t the focus of simulation output analysis.