Definition 12.15 (independence). Two events A,B are independent exactly when
P(A∩B)=P(A)⋅P(B).
When P(A),P(B)>0, this is exactly equivalent to P(B/A)=P(B) and P(A/B)=P(A) -- knowing A happened tells you nothing new about B's chances, and vice versa. This is the special case of the Multiplication Theorem where the conditional term collapses to the plain probability. Mutually independent events A1,…,An satisfy the stronger requirement that EVERY sub-collection multiplies out: P(Ai1∩⋯∩Aik)=P(Ai1)⋯P(Aik).
Theorem 12.8. If A,B are independent, then so are (i) Aˉ,B; (ii) A,Bˉ; (iii) Aˉ,Bˉ. (Proved via De Morgan's law: P(Aˉ∩Bˉ)=P(A∪B)=1−P(A∪B)=1−[P(A)+P(B)−P(A)P(B)]=[1−P(A)][1−P(B)]=P(Aˉ)P(Bˉ).)
Theorem 12.9. For events with P(A)=0, P(B)=0: (1) mutually exclusive events can NEVER be independent, and (2) independent events can NEVER be mutually exclusive. The reason is direct: independence needs P(A∩B)=P(A)P(B)>0 (a genuine positive overlap), while mutual exclusivity needs P(A∩B)=0 -- the two conditions contradict each other unless one event is essentially impossible.
Independence is a probability property, mutual exclusivity is a set-theoretic property. Whether two events are independent can only be checked from their PROBABILITIES (P(A∩B) vs P(A)P(B)); whether they are mutually exclusive is checked from the EVENTS themselves (A∩B=∅ or not). Typical applications: independent coin tosses/die rolls/card draws WITH replacement; a card draw WITHOUT replacement is generally NOT independent, since removing the first card changes the composition available for the second.