Summary
This chapter provides a comprehensive introduction to probability, including the formal definitions of experiments, sample spaces, and events. It emphasizes the importance of counting rules (multiplication, combinations, and permutations) to enumerate sample points. Various methods for assigning probabilities are discussed, followed by key probability laws such as complement, addition, and multiplication. The chapter further explores conditional probabilities, independence of events, and concludes with Bayes’ theorem, outlining how to update prior probabilities into posterior probabilities when new information becomes available. Understanding these fundamental concepts is essential for effective decision analysis in both statistical and business contexts.