Probability

Linearity of Expectation

Expectations of a sum equal the sum of expectations, even when the variables are dependent.

E[aX + bY] = a E[X] + b E[Y], with no independence assumption anywhere.

That is the whole point, and it is why linearity solves problems that look intractable. To find the expected number of something, write it as a sum of indicator variables and add up their individual probabilities - the dependence between them never enters.

The trap. It does not extend to products (E[XY] = E[X]E[Y] needs independence) or to variance (Var(X+Y) = Var(X)+Var(Y) needs zero correlation). Candidates who internalise "no independence needed" often leak it into variance, which interviewers test deliberately.

Full guide

Linearity of Expectation: The Most Useful Trick in Quant Interviews

Expectations add even when the events are dependent. Why that is surprising, and the interview problems it solves in one line.

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