Introduction to Probability

Probability is a branch of mathematics that deals with the likelihood or chance of events occurring. It provides a framework for quantifying uncertainty and making informed decisions in the face of randomness. Probability theory is widely used in various fields, including statistics, science, engineering, finance, and artificial intelligence.

Key Concepts in Probability:

  1. Experiment:
  • An experiment is a process that produces an outcome. It could be a coin toss, rolling a die, or conducting a scientific experiment.
  1. Sample Space (S):
  • The sample space is the set of all possible outcomes of an experiment. It is denoted by (S).
  1. Event:
  • An event is a subset of the sample space, representing a specific outcome or a collection of outcomes.
  1. Probability (P):
  • Probability is a numerical measure of the likelihood of an event occurring. It ranges from 0 (impossible) to 1 (certain).
  • Probability is denoted by (P) and is calculated as the ratio of the number of favorable outcomes to the total number of possible outcomes. [ P(A) = \frac{\text{Number of favorable outcomes for event } A}{\text{Total number of possible outcomes}} ]
  1. Complement of an Event ((A’) or (A^c)):
  • The complement of an event (A) is the set of all outcomes in the sample space that are not in (A).
  1. Mutually Exclusive Events:
  • Two events are mutually exclusive (or disjoint) if they cannot occur at the same time.
  1. Independent Events:
  • Two events are independent if the occurrence of one event does not affect the occurrence of the other.

Probability Rules:

  1. Addition Rule:
  • ( P(A \cup B) = P(A) + P(B) – P(A \cap B) )
  • The probability of the union of two events is the sum of their individual probabilities minus the probability of their intersection.
  1. Multiplication Rule for Independent Events:
  • ( P(A \cap B) = P(A) \cdot P(B) )
  • The probability of the intersection of two independent events is the product of their individual probabilities.
  1. Conditional Probability:
  • ( P(A|B) = \frac{P(A \cap B)}{P(B)} )
  • The probability of event (A) given that event (B) has occurred.
  1. Law of Total Probability:
  • ( P(A) = P(A \cap B_1) + P(A \cap B_2) + \ldots + P(A \cap B_n) )
  • The probability of event (A) is the sum of the probabilities of (A) intersecting with each mutually exclusive event (B_i).


Consider rolling a fair six-sided die. The sample space (S) is ({1, 2, 3, 4, 5, 6}). The probability of rolling an even number ((A)) is (P(A) = \frac{3}{6} = \frac{1}{2}).

If we define (B) as the event of rolling a number less than 4, then (P(B) = \frac{3}{6} = \frac{1}{2}). The intersection of (A) and (B) is the event of rolling an even number less than 4, which is ({2}). Therefore, (P(A \cap B) = \frac{1}{6}).

The complement of (A) is the event of rolling an odd number, denoted as (A’). (P(A’) = 1 – P(A) = \frac{1}{2}).

These concepts and rules provide a foundation for more advanced topics in probability theory, such as conditional probability, Bayes’ theorem, and probability distributions. Probability theory is a fundamental tool for reasoning about uncertainty and randomness in diverse fields.