Conditional Probability
Conditional probability answers "what is the chance of A given that B has already happened?" The condition shrinks the sample space to only the outcomes where B is true, so P(A|B) = P(A∩B)/P(B). The clearest CAT signal is the phrase "given that" or any setup where one event is already known to have occurred. With cards and dice the fast method is to recount directly inside the reduced space: if a die shows an even number, there are only 3 outcomes left {2,4,6}, so the chance it is also greater than 3 is just 2/3. Drawing "without replacement" is conditional by nature — the second draw’s probabilities depend on what the first draw removed. Rearranging the formula gives the multiplication rule P(A∩B) = P(B)·P(A|B), the engine behind sequential draws.
✅ Solved examples
✏️ Practice — try these, take hints as needed
📝 Topic test — 8 questions
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Formula Reference Sheet
Core probability rules
| Classical probability | P(E) = favourable / total |
|---|---|
| Range of probability | 0 ≤ P(E) ≤ 1 |
| Complement rule | P(E′) = 1 − P(E) |
| Addition rule | P(A∪B) = P(A) + P(B) − P(A∩B) |
| Mutually exclusive | P(A∩B) = 0 ⇒ P(A∪B) = P(A) + P(B) |
Conditional, independence & Bayes
| Conditional probability | P(A|B) = P(A∩B) / P(B) |
|---|---|
| Multiplication rule | P(A∩B) = P(B) × P(A|B) |
| Independent events | P(A∩B) = P(A) × P(B) |
| Total probability | P(A) = P(B)·P(A|B) + P(B′)·P(A|B′) |
| Bayes’ theorem | P(B|A) = P(B)·P(A|B) / P(A) |