StatReasonWhat this answers
This calculator answers "roughly how likely is this event, estimated by simulating it directly many times, rather than solving for it with a formula?" useful both as a check on an analytical answer and for events complex enough that a clean formula is not obvious.
How it is calculated
Each trial simulates one full draw from your chosen model, using a seeded, fully reproducible pseudorandom generator, and checks whether your defined event occurred. The estimated probability is simply the fraction of trials where it did. The Monte Carlo standard error quantifies how much that estimate itself would vary if you reran the whole simulation with a different seed, shrinking as you increase the number of trials.
Worked example
For a fair six-sided die simulated as a custom discrete outcome with equal probabilities, estimating the probability of rolling a 6 over 20,000 simulated rolls gives an estimate very close to the true value of 1 in 6, with a Monte Carlo standard error small enough to confirm the simulation has converged to a stable answer at that many trials.
Assumption audit
What this result does not mean
A Monte Carlo estimate is not a forecast or a guarantee about any single real-world outcome; it estimates a probability under your stated model, with a quantified amount of simulation error, not certainty.
Common mistakes
Running too few trials and treating a noisy early estimate as final is a common mistake; the Monte Carlo standard error tells you exactly how much simulation noise remains, and it is worth checking before trusting a surprising result. A second mistake is modeling dependent, multi-step real-world processes as if a single simple binomial, normal, or discrete draw fully captures them, when the actual process may have structure this launch model set does not represent.
Limitations
This calculator supports only the binomial, normal, and custom discrete PMF models at launch; more elaborate multi-step or dependent-event simulations are out of scope for this specific engine.