Monte Carlo simulation.

In plain English

Monte Carlo simulation replaces a single assumed return with thousands of randomly generated return paths, then reports how often each outcome occurred across all of those runs. Retirement software uses it to estimate the share of trials in which savings last a chosen number of years. The output is a distribution rather than one number, usually summarized as a success rate and a set of percentile balances. Results depend entirely on the assumptions fed in: expected return, volatility, inflation, spending, and how the random draws are shaped. Standard versions assume returns are independent from year to year, which understates the damage of several bad years arriving in a row.

01Why it matters

A single average-return projection hides the fact that the order of returns matters, and a simulation makes the range of real outcomes visible instead of one tidy line.

02The math, step by step

A plan runs 10,000 trials and the money lasts to age 95 in 8,500 of them, an 85 percent success rate. The median ending balance might be 400,000 while the tenth percentile runs out eight years early. The average outcome and the bad outcome tell different stories about the same plan.

Illustrative example. The amounts here are hypothetical, chosen to show how the math works, not real quoted rates or figures.

03What this is NOT

04Receipts

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