PSTAT 5A: Introduction to Probability

Lecture 4: Fundamentals of Probability, Rules , Axioms & Properties

Narjes Mathlouthi

2025-07-01

Today’s Learning Objectives

By the end of this lecture, you will be able to:

What is Probability?

🎯 Definition

Probability is a measure of the likelihood that an event will occur

Probability Range

  • Ranges from 0 to 1 (or 0% to 100%)

  • 0: Event will never occur (impossible)

  • 1: Event will certainly occur (certain)

  • 0.5: Event has equal chance of occurring or not

Three Ways to Express Probability

  • Fraction: \frac{1}{2}, \frac{3}{4}, \frac{2}{6}
  • Decimal: 0.5, 0.75, 0.33
  • Percentage: 50%, 75%, 33%

Example

When we roll a die, there are six possible outcomes:

1, 2, 3, 4, 5, 6.

The probability of any of them turning up is 1/6 or 16%.

Why Study Probability?

Probability helps us:

  • Make decisions under uncertainty
  • Understand random processes
  • Analyze data and draw conclusions
  • Model real-world phenomena
  • Assess risk and likelihood

Applications: Weather forecasting, medical diagnosis, finance, quality control, gaming, insurance

Random Experiments

A random experiment is a process that:

  1. Can be repeated under similar conditions
  2. Has multiple possible outcomes
  3. The outcome cannot be predicted with certainty

Examples

  • 🪙 Flipping a coin

  • 🎲 Rolling a die

  • 🃏 Drawing a card from a deck

  • 💡 Measuring the lifetime of a light bulb

Sample Space

🎯 Definition The sample space (denoted S or \Omega) is the set of all possible outcomes of a random experiment

Sample Space Examples

  • Coin flip: S = \{H, T\}
  • Two coin flips: S = \{HH, HT, TH, TT\}
  • 🎲 Die roll: S = \{1, 2, 3, 4, 5, 6\}
  • Two die rolls
  • S = \{A\heartsuit,\ 2\heartsuit,\ \dots,\ K\heartsuit,\\ \phantom{S = \{}A\diamondsuit,\ 2\diamondsuit,\ \dots,\ K\diamondsuit,\\ \phantom{S = \{}A\clubsuit,\ 2\clubsuit,\ \dots,\ K\clubsuit,\\ \phantom{S = \{}A\spadesuit,\ 2\spadesuit,\ \dots,\ K\spadesuit\}

Types of Sample Spaces

Finite Sample Space

  • Limited number of outcomes
    • Example: Rolling a die

Infinite Sample Space

  • Unlimited outcomes (countable or uncountable)
    • Example: Measuring exact height of students

Events

🎯 Definition An event is a subset of the sample space

  • Simple event: Contains exactly one outcome (Ex: A = \{3\} (rolling a 3))

  • Compound event: Contains multiple outcomes (Ex: B = \{2, 4, 6\} (rolling an even number))

Event Notation

For a die roll with S = \{1, 2, 3, 4, 5, 6\}:

  • A = \{1, 3, 5\} (rolling an odd number)
  • B = \{4, 5, 6\} (rolling 4 or higher)
  • C = \{6\} (rolling a six)

We can describe events in words or using set notation

Set Operations Overview

🎯 Definition:

  • Set: Collection of distinct objects
  • Union: A OR B occurs
  • Intersection: A AND B occurs
  • Complement: A does NOT occur
  • Sample Space: All possible outcomes

What is a Set?

🎯 Definition: A collection of things that share common characteristics. They can be elements, members, objects or similar terms.

Examples:

  • Set of even numbers:
    • {2, 4, 6, 8, …}
  • Set of vowels: {a, e, i, o, u}

Union: A ∪ B

🎯 Definition: Contains all set elements, including intersections.

In Probability: The event that A OR B occurs (or both).

P(A \cup B) = P(A) + P(B) - P(A \cap B)

Intersection: A ∩ B

🎯 Definition: Area where two or more sets overlap.

In Probability: The event that A AND B occurs.

Properties:

  • Always smaller than or equal to individual sets

  • Can be empty (disjoint sets)

Absolute Complement: A^c

🎯 Definition: All elements that do not belong to the set.

In Probability: The event that A does NOT occur.

  • P(A^c) = 1 - P(A)

Key Property:

A \cup A^c = S (Sample Space)

Summary Table

Operation Symbol Meaning Probability
Union A \cup B Occurs if A or B P(A \cup B) = P(A) + P(B) - P(A \cap B)
Intersection A \cap B Occurs if A and B P(A \cap B)
Complement A^c Occurs if A does not occur P(A^c) = 1 - P(A)

Probability Axioms: Commutative

Commutative

A \cup B = B \cup A

A \cap B = B \cap A

Probability Axioms: Associative

Associative

(A \cup B) \cup C = A \cup (B \cup C)

(A \cap B) \cap C = A \cap (B \cap C)

Probability Axioms: Distributive

Distributive

A \cup (B \cap C) = (A \cup B) \cap (A \cup C)

A \cap (B \cup C) = (A \cap B) \cup (A \cap C)

Probability Axioms: De Morgan’s Laws

De Morgan’s Laws

(A \cup B)^c = A^c \cap B^c

(A \cap B)^c = A^c \cup B^c

Practice Examples

Example 1: In a class of students:

  • Set A = Students who like Math

  • Set B = Students who like Science

Q: What does A ∪ B represent?

Solution. Students who like Math OR Science (or both)

Example 2: What does A ∩ B represent?

Solution. Students who like BOTH Math AND Science

Example 3: What does A^c represent?

Solution. Students who do NOT like Math

Example: Set Operations

For die roll S = \{1, 2, 3, 4, 5, 6\}:

  • A = \{1, 3, 5\} (odd numbers)

  • B = \{4, 5, 6\} (4 or higher)

Find:

  • A \cup B

  • A \cap B

  • A^c

Solution.

  • A \cup B = \{1, 3, 4, 5, 6\}

  • A \cap B = \{5\}

  • A^c = \{2, 4, 6\}

Mutually Exclusive Events

Events A and B are mutually exclusive (or disjoint) if they cannot occur simultaneously

A \cap B = \emptyset

When rolling a die

  • A = \{1, 3, 5\} (odd)

  • B = \{2, 4, 6\} (even)

A and B are mutually exclusive

The Classical Definition of Probability

🎯 Definition: For equally likely outcomes:

P(A) = \frac{\text{Number of outcomes in } A}{\text{Total number of outcomes in } S}

Probability of rolling an even number on a fair die

P(\text{even}) = \frac{3}{6} = \frac{1}{2}

Properties of Probability

  1. Non-negativity: P(A) \geq 0 for any event A

  2. Normalization: P(S) = 1

  3. Additivity: If A and B are mutually exclusive, then

P(A \cup B) = P(A) + P(B)

The Complement Rule

P(A) + P(A^c) = 1

P(A^c) = 1 - P(A)

If the probability of rain is 0.3, what’s the probability of no rain?

Solution. P(\text{no rain}) = 1 - P(\text{rain}) = 1 - 0.3 = 0.7

Questions?

Office Hours: Thursday’s 11 AM On Zoom (Link on Canvas)

Email: nmathlouthi@ucsb.edu

Next Class: Conditional Probabilities & Independence

Resources

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