Conditional probability measures event likelihood given another occurred. P(A|B) = P(A ∩ B) / P(B). Key concepts: reduces sample space to condition B, must have P(B) > 0. Common traps: confusing conditional with joint probability, wrong denominator. Exam tips: identify condition carefully, organize information clearly. Time-saving: use Venn diagrams or tables. Example: if P(A ∩ B) = 0.12 and P(B) = 0.4, then P(A|B) = 0.12/0.4 = 0.3. Independence check: events independent if P(A|B) = P(A). Tree diagrams: visualize conditional scenarios. Applications: medical testing (positive test given disease), customer behavior analysis. Bayes theorem: relates P(A|B) to P(B|A). Understanding conditional probability essential for real-world problems. Practice with practical scenarios.