Lecture 4: Fundamentals of Probability, Rules , Axioms & Properties
2025-07-01
By the end of this lecture, you will be able to:
Probability is a measure of the likelihood that an event will occur
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
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%.
Probability helps us:
Applications: Weather forecasting, medical diagnosis, finance, quality control, gaming, insurance
A random experiment is a process that:
Examples
🪙 Flipping a coin
🎲 Rolling a die
🃏 Drawing a card from a deck
💡 Measuring the lifetime of a light bulb
🎯 Definition The sample space (denoted S or \Omega) is the set of all possible outcomes of a random experiment
Finite Sample Space
Infinite Sample Space
🎯 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))
For a die roll with S = \{1, 2, 3, 4, 5, 6\}:
We can describe events in words or using set notation
🎯 Definition:
🎯 Definition: A collection of things that share common characteristics. They can be elements, members, objects or similar terms.
Examples:
🎯 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)
🎯 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)
🎯 Definition: All elements that do not belong to the set.
In Probability: The event that A does NOT occur.
Key Property:
A \cup A^c = S (Sample Space)
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) |
Commutative
A \cup B = B \cup A
A \cap B = B \cap A
Associative
(A \cup B) \cup C = A \cup (B \cup C)
(A \cap B) \cap C = A \cap (B \cap C)
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)
De Morgan’s Laws
(A \cup B)^c = A^c \cap B^c
(A \cap B)^c = A^c \cup B^c
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
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\}
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
🎯 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}
Non-negativity: P(A) \geq 0 for any event A
Normalization: P(S) = 1
Additivity: If A and B are mutually exclusive, then
P(A \cup B) = P(A) + P(B)
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
Office Hours: Thursday’s 11 AM On Zoom (Link on Canvas)
Email: nmathlouthi@ucsb.edu
Next Class: Conditional Probabilities & Independence
Understanding Data - Introduction to Probability © 2025 Narjes Mathlouthi