Probability

Bayes' Theorem

Also known as: Bayes' Rule

A rule for updating the probability of a hypothesis when new evidence arrives.

P(H | E) = P(E | H) P(H) / P(E).

In words: posterior is proportional to likelihood times prior.

The practical technique for interviews is to abandon percentages and use natural frequencies. Imagine 100,000 people, count how many land in each cell, and take the ratio. The arithmetic stops being confusing almost immediately.

The trap. The base rate dominates when the event is rare. A 99%-accurate test for a 1-in-1,000 condition yields a positive result that is genuine less than 10% of the time, because false positives are drawn from a vastly larger population than true positives.

Full guide

Bayes' Theorem for Quant Interviews

The base rate is the whole question. How to set up Bayes problems so the arithmetic is trivial, and why the famous test-accuracy trap works.

Related terms

Practise this

Put it into practice

Knowing the definition is not the same as spotting where it applies under time pressure. Work the question bank free.

Start practising free

Browse the full quant interview glossary