Monte Carlo Analysis

Rather than plugging one set of numbers into a model to get one answer, a Monte Carlo simulation varies the uncertain inputs across their plausible ranges and runs the model many thousands of times. The result is a distribution: not “the project will return 18%,” but “there is roughly a 70% chance the return falls between 12% and 24%.”

For strategy and business cases, this reframes planning around probability and risk rather than false precision, which is closer to how the future actually behaves.