Exam P Syllabus Explained

The July 2026 syllabus weights general probability at 23-30%, univariate random variables at 44-50%, and multivariate random variables at 23-30%. That means distributions, expected value, variance, and transformations deserve the most practice time.

Tier 1 topics are expected value, variance, distributions, conditional probability, Bayes theorem, and CLT. Tier 2 topics are joint distributions, covariance, correlation, and insurance payment random variables. Tier 3 topics are combinatorics, order statistics, and narrower distribution tricks.

Study conditional probability before Bayes theorem, expected value before variance shortcuts, discrete distributions before continuous distributions, and joint distributions before covariance and correlation.

Suppose a candidate has six review weeks and is weak on both combinatorics and expected value. The syllabus weights make the first decision clear: expected value touches distributions, variance, conditional expectation, insurance payments, and multivariate work, so it gets the first block of review time.

Combinatorics still matters, but it should not crowd out the central random-variable block. A practical split is three weeks on distribution and moment work, two weeks on conditional and multivariate probability, and one rotating week for narrower general-probability gaps plus mixed practice.