Page 2: Sample Spaces and the Law of Large Numbers

This page delves into the concept of sample spaces and introduces the crucial Law of Large Numbers, using coin flips as practical examples to demonstrate probability principles.

Definition: Sample space (S) represents the complete set of possible outcomes for a random phenomenon.

Example: For a single coin flip, S = {H,T}, while for two flips, S = {HH, TT, HT, TH}.

Highlight: The Law of Large Numbers states that as trials increase, the proportion of occurrences tends to stabilize around the true probability.

Quote: "The 'Law of Averages' does not exist" - emphasizing the importance of understanding true probability principles.

Page 3: Probability Modeling and Theoretical Calculations

This page focuses on the mathematical aspects of probability modeling, introducing theoretical probability calculations and fundamental probability rules.

Definition: Theoretical probability is derived from mathematical models rather than observations, calculated as P(A) = (number of favorable outcomes)/(total number of possible outcomes).

Vocabulary: Disjoint events are those that cannot occur simultaneously.

Highlight: Key probability rules include:

• 0 ≤ P(A) ≤ 1 for any event A
• P(S) = 1 for the entire sample space
• P(A) = 1 - P(A') for complementary events
• P(A∪B) = P(A) + P(B) for disjoint events

Example: Probability calculations often result in fractions that can be converted to decimals or percentages.