Which descriptive statistics are used for simulation output analysis according to the material?

• A. To generate random samples.
• B. To calculate mean, standard deviation, and probability/proportion.
• C. To adjust model assumptions.
• D. To optimize parameters.

Answer: (B)

In Monte Carlo simulation, you summarize the results of many runs with descriptive statistics to understand what the model produces across scenarios. The mean gives the central tendency—the estimated expected outcome across runs. The standard deviation measures variability or risk by showing how spread out the results are around that average. Probability or proportion helps quantify how often a particular event occurs, such as the probability that profit exceeds a target, by looking at the fraction of runs that meet the condition. Together, these statistics describe the output distribution and support informed decisions without assuming a specific shape for that distribution. Generating random samples is about creating the inputs for the simulation, while adjusting model assumptions or optimizing parameters are calibration or fitting activities, not ways of describing the simulated outputs.