Which statement best describes a Monte Carlo simulation?

• A. It uses deterministic methods to predict exact outcomes.
• B. It ignores all uncertainty.
• C. It uses random sampling to estimate the impact of uncertainty on outcomes.
• D. It yields a single point estimate without distributions.

Answer: (C)

Monte Carlo simulation models uncertainty by using random sampling to propagate input variability through a model and examine the resulting distribution of outcomes. By drawing many random inputs from specified distributions and running the model each time, you build up a picture of how results behave under uncertainty. This lets you estimate not just a single value but the range and likelihood of possible outcomes, providing measures like the mean, spread, and percentiles, as well as risk metrics such as probability of loss or VaR.

In practice, you specify distributions for the uncertain drivers (like sales, costs, or discount rates), run thousands of iterations, and summarize the results to understand how sensitive the outcome is to each input and what the probability is of various scenarios. This approach contrasts with deterministic methods, which fix inputs and yield a single outcome, and with methods that ignore uncertainty or produce only one point estimate.

For example, when estimating a project’s net present value, you’d sample cash flows and the discount rate from their distributions, compute NPV for each run, and then analyze the resulting distribution to see how likely different NPV levels are. That ongoing exploration of how randomness affects results is exactly what Monte Carlo simulation provides.