Quant Memo
Foundational

Anchor Numbers Worth Memorising

A short list of everyday quantities — populations, areas, prices — that let you build a Fermi estimate quickly by scaling from something you already know cold.

Estimation interview questions ("how many piano tuners are in Chicago?") aren't really testing whether you know the answer — they're testing whether you can build one from a small set of reliable reference points. Having a handful of anchor numbers memorized cold turns a blind guess into a chain of reasonable multiplications, and interviewers care far more about the chain than the final digit.

A useful starter set: US population ≈ 330 million, world population ≈ 8 billion, an average household size ≈ 2.5 people, a car's average lifespan ≈ 15 years, a major US city's land area ≈ 200-600 square miles, and an average adult walking speed ≈ 3 miles per hour. None of these need to be exact — they need to be in the right order of magnitude and easy to recall under pressure.

A worked example

"How many gas stations are in the US?" Start from population (330 million), assume roughly 1 car per 2 people (165 million cars), assume each car fills up roughly once a week and each station serves maybe 500 fill-ups a week across its pumps — that's 165,000,000 / 7 fill-ups per day ≈ 23.6 million per day, divided by a station's daily throughput of maybe 500-1000 cars, landing around 25,000-45,000 stations. The real US figure is roughly 145,000, so this chain undercounts by a few times — a normal outcome for a first pass, and exactly the kind of gap an interviewer expects you to sanity-check and adjust out loud.

A small set of memorized anchor numbers — population, household size, typical speeds and lifespans — turns an estimation question into a chain of defensible multiplications, and getting the order of magnitude roughly right matters far more than precision.

Related concepts

Practice in interviews

Further reading

  • Guesstimation, Weinstein & Adam
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