think-fermi-estimationlisted

<!-- thinking-framework-skills | https://github.com/product-on-purpose/thinking-framework-skills | Apache-2.0 --> # 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,