What does Monte Carlo simulation provide in Mega Goal risk assessment?

• A. A single best-case date
• B. A probability distribution of completion times and risks through random sampling
• C. A budget baseline
• D. A quality score

Answer: (B)

Monte Carlo simulation builds a picture of uncertainty by treating uncertain inputs as probability distributions and drawing random samples from them many times. In Mega Goal risk assessment, this means modeling durations, costs, and risk events with their likely ranges and repeating the project model thousands of times. Each run yields a completion date and a level of risk; collecting all runs creates a distribution of possible outcomes rather than a single number.

This distribution lets you see how often you might meet a milestone, how likely overruns are, and how big the potential surprises could be. It also supports identifying which inputs most drive variability, so you can focus on the factors that matter most. A single best-case date, a fixed budget baseline, or a quality score don’t convey this range of possibilities and the probabilistic insight Monte Carlo provides.