In simulation, what does randomly generated values refer to?

In simulation, what does randomly generated values refer to?

• A. Fixed historical values
• B. Single-point estimates
• C. Values for random variables that are produced from specified probability distributions
• D. Deterministic trend lines

Answer: (C)

In simulation, randomly generated values come from probability distributions that describe uncertainty about inputs. Instead of plugging in fixed numbers, you assign each uncertain input a distribution and draw a random value from it for each run. Repeating this across many runs propagates that uncertainty through the model, producing a range of possible outcomes and their probabilities. That’s exactly what “values for random variables produced from specified probability distributions” means in practice. The other ideas describe constants or single-path forecasts: fixed historical values are steady numbers with no randomness, single-point estimates are just one guess per input, and deterministic trend lines follow one fixed path without variability. None of these capture how uncertainty influences results the way sampling from distributions does. For this reason, the distribution-based random draws are the core mechanism behind Monte Carlo simulation.

In simulation, randomly generated values come from probability distributions that describe uncertainty about inputs. Instead of plugging in fixed numbers, you assign each uncertain input a distribution and draw a random value from it for each run. Repeating this across many runs propagates that uncertainty through the model, producing a range of possible outcomes and their probabilities. That’s exactly what “values for random variables produced from specified probability distributions” means in practice.

The other ideas describe constants or single-path forecasts: fixed historical values are steady numbers with no randomness, single-point estimates are just one guess per input, and deterministic trend lines follow one fixed path without variability. None of these capture how uncertainty influences results the way sampling from distributions does. For this reason, the distribution-based random draws are the core mechanism behind Monte Carlo simulation.