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A Monte Carlo forecast summarised analytically: with a normally distributed outcome, the probability of beating a target is the normal tail above the target, and the confidence interval is the mean plus or minus z times the standard deviation. That gives the same headline figures a simulation reports, without the runs.
Probability of exceeding target
P(X > target) = 1 - Phi((Target - Mean) / Standard deviation)
Confidence bounds
Bound = Mean +/- z x Standard deviation, with z = 1.645 at 95%
When outcomes are bounded, skewed or driven by a few binary events. Then run an actual simulation with the right distributions.
From historical forecast error, or by treating the best-to-worst range as roughly six standard deviations as a first approximation.