Which approach helps fill knowledge gaps in uncertain planning when data is scarce?

• A. Ignoring uncertainty
• B. Using Monte Carlo simulation to model risk
• C. Relying solely on guesswork
• D. Copying another project’s plan without adjustment

Answer: (B)

When planning with limited data, you want to represent what you don’t know and understand how that uncertainty affects outcomes. Monte Carlo simulation does exactly that by treating uncertain inputs as probability distributions and repeatedly sampling from them to build a distribution of possible results. With many runs, you don’t just get a single forecast; you get a range of outcomes with associated probabilities, which shows the likelihood of finishing on time or over budget and reveals which inputs drive the most risk. This approach works even when data is scarce because you can start with broad, expert-informed distributions and refine them as new information comes in, using the simulations to highlight where more data collection or contingency planning would make the biggest difference. In contrast, ignoring uncertainty hides risk, relying on guesswork is unreliable, and copying another project without adjustment ignores both your context and the uncertainties you face.