Conditional probability is the probability of an event given that another event has occurred, written P(A | B). Bayes' theorem is the formal rule for inverting that conditional — computing P(A | B) from P(B | A), the prior P(A), and the marginal P(B).
What this topic covers
This topic covers the definition of conditional probability, the multiplication rule, the law of total probability, Bayes' theorem in both its 2-event and partition forms, and the standard set of trap problems: the Monty Hall family, false-positive medical-test problems, the two-children problem, and prosecutor's fallacy. Each trap exposes a different intuitive error that interviewers deliberately probe.
Why it matters for quant interviews
Conditional probability and Bayes' theorem are tested in nearly every quant interview at every top firm, and they are the single most common source of wrong answers from otherwise strong candidates. Mastery of this topic unlocks roughly a third of all probability interview questions and is foundational for the statistics and stochastic-processes weeks that follow.