Fermi Estimation

Sometimes you need a number and there is nothing to look up: no dataset, no genuine reference class, no precedent to borrow. A single all-at-once guess at the whole magnitude is badly anchored and hides its own uncertainty. The Fermi move is to factor the unknown into a short chain of sub-quantities - each one small and familiar enough to guess to within a factor - then multiply the chain back into an estimate and compound the per-factor bands into a low/high range. The reason it can beat one wild guess is partial error cancellation: if the per-factor errors are roughly independent and centered, over-guessing one factor and under-guessing another tend to offset in the product. The output is a Fermi decomposition worksheet, not a lone number. The honest constraint: the cancellation only works when the factors are independent, and the benefit is real mainly for large, unfamiliar quantities - not ordinary ones you could estimate directly.

When to Use

• You need a numeric magnitude and no lookup-able data and no genuine reference class exists, so the number has to be built from factors.
• The quantity is large and unfamiliar (market size, total load, total cost, a conversion count you cannot look up) - the regime where decomposition actually helps.
• An order-of-magnitude answer with an honest band is useful for sizing, sanity-checking, or triage; the number does not have to be exact.
• You want the estimate inspectable: each factor, its basis, and its band exposed so a reader can challenge one number, not an opaque total.

When NOT to Use

• A genuine reference class with real base-rate data exists. Then anchor on that data, not on invented factors - use think-reference-class-forecasting. Fermi is precisely the build-from-factors method for when no such class exists; if you have real base rates, reference-class forecasting is strictly better.
• The task only needs the question decomposed for coverage, not a number. If you want a mutually-exclusive, collectively-exhaustive breakdown of a question and explicitly no estimate, use think-issue-tree, which produces a tree and produces no number. Fermi exists to produce a number; do not use it when a number is not wanted.
• The quantity is ordinary and familiar. Decomposing something you could estimate directly adds noise; the decomposition benefit was absent or negative in that regime (see evidence/dossier.md).
• The factors share a driver (correlated). Multiplicative error-cancellation fails when factors move together; the chain can be worse than one careful guess. Flag it and restructure to independent factors, or stop.
• Never emit a point estimate with no low/high band. A Fermi number without its range hides the uncertainty the method exists to expose.