Distributions are functions that assign probabilities to outcomes of a random variable. The discrete and continuous distributions covered in this topic — Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, Uniform, Exponential, and Normal — are the building blocks for every probability model used in quant interviews.
What this topic covers
This topic covers the standard discrete and continuous distributions and the relationships between them: Poisson as a Binomial limit, Exponential as the memoryless continuous analog of Geometric, Normal as the limiting distribution via the Central Limit Theorem. It also introduces moment-generating functions at the level needed to derive sums of independent random variables.
Why it matters for quant interviews
Interviewers expect not just memorized formulas but the ability to recognize which distribution applies from a verbal description and to compute expectations and probabilities from first principles. This setup is used heavily in Week 1 Topics 4–6 and throughout Weeks 2 and 3 — getting it cold here pays back across the whole curriculum.