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AP Statistics Chapter 5 Notes: Probability: What are the Chances? Page 1 of 3
AP Statistics Chapter 5 Probability: What are the Chances?
5.1: Randomness, Probability and Simulation
Probability
The probability of any outcome of a chance process is a number between 0 and 1 that describes
the proportion of times the outcome would occur in a very long series of repetitions.
Simulation
The imitation of chance behavior, based on a model that accurately reflects the situation, is
called a simulation.
Performing of a Simulation The 4-Step Process
1. State: Ask a question of interest about some chance process.
2. Plan: Describe how to use a chance device to imitate one repetition of the process. Tell
what you will record at the end of each repetition.
3. Do: Perform many repetitions of the simulation.
4. Conclude: Use the results of your simulation to answer the question of interest.
5.2: Probability Rules
Sample Space
The sample space S of a chance process is the set of all possible outcomes.
Probability Models
Descriptions of chance behavior contain two parts:
A probability model is a description of some chance process that consists of two parts:
a sample space S and
a probability for each outcome.
For example: When a fair 6-sided die is rolled, the Sample Space is S = {1, 2, 3, ,4,5, 6}.
The probability for a fair die would include the probabilities of these outcomes, which are all the
same.
Outcome
1
2
3
4
5
6
Probability
1/6
1/6
1/6
1/6
1/6
1/6
Event
An event is any collection of outcomes from some chance process. That is, an event is a subset
of the sample space. Events are usually designated by capital letters, like A, B, C, and so on.
For example: For the probability model above we might define event A = die roll is odd. The
elements of the sample space S that fits this event are {1, 3, 5}. The probability of the event A,
written as P(A) is the 3/6 or ½. So we would write P(A) = 0.5, in decimal form.
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AP Statistics Chapter 5 – Probability: What are the Chances?

5.1: Randomness, Probability and Simulation

Probability

The probability of any outcome of a chance process is a number between 0 and 1 that describes the proportion of times the outcome would occur in a very long series of repetitions.

Simulation

The imitation of chance behavior, based on a model that accurately reflects the situation, is called a simulation.

Performing of a Simulation – The 4-Step Process

1. State : Ask a question of interest about some chance process. 2. Plan : Describe how to use a chance device to imitate one repetition of the process. Tell what you will record at the end of each repetition. 3. Do : Perform many repetitions of the simulation. 4. Conclude : Use the results of your simulation to answer the question of interest.

5.2: Probability Rules

Sample Space

The sample space S of a chance process is the set of all possible outcomes.

Probability Models

Descriptions of chance behavior contain two parts:

A probability model is a description of some chance process that consists of two parts:

  • a sample space S and
  • a probability for each outcome.

For example: When a fair 6-sided die is rolled, the Sample Space is S = {1, 2, 3, ,4,5, 6}. The probability for a fair die would include the probabilities of these outcomes, which are all the same.

Outcome 1 2 3 4 5 6 Probability 1/6 1/6 1/6 1/6 1/6 1/

Event An event is any collection of outcomes from some chance process. That is, an event is a subset of the sample space. Events are usually designated by capital letters, like A, B, C , and so on.

For example : For the probability model above we might define event A = die roll is odd. The elements of the sample space S that fits this event are {1, 3, 5}. The probability of the event A, written as P(A) is the 3/6 or ½. So we would write P(A) = 0.5, in decimal form.

The Basic Rules of Probability

  • For any event A , 0 ≤ P ( A ) ≤ 1.
  • If S is the sample space in a probability model, P ( S ) = 1.
  • In the case of equally likely outcomes,

totalnumberofoutcomesinsamplespace

numberofoutcomescorrespondingtoevent ( )

A

P A 

  • Complement rule: P ( AC ) = 1 – P ( A )
  • Addition rule for mutually exclusive events: If A and B are mutually exclusive, P ( A or B ) = P ( A ) + P ( B ). Also be familiar with the notation: 𝑷(𝑨 ∪ 𝑩).

Mutually Exclusive Events Two events A and B are mutually exclusive (or disjoint ) if they have no outcomes in common and so can never occur together—that is, if P ( A and B ) = 0. Alternate notation: 𝑷(𝑨 ∩ 𝑩).

For example : Using a deck of playing cards and drawing a card at random, the events A = card is a King, and B = card is a Queen are mutually exclusive because a single card cannot be both a King and a Queen. Thus we can calculate the probability of A or B as the sum of their individual probabilities - P(A or B) = P(A) + P(B).

General Addition Rule If A and B are any two events resulting from some chance process, then P ( A or B ) = P ( A ) + P ( B ) – P ( A and B )

Venn Diagrams and Probability

The complement Ac^ contains exactly the outcomes that are not in A.

The events A and B are mutually exclusive (disjoint) because they do not overlap. That is, they have no outcomes in common.

The intersection of events A and B ( A B ) is the set of all outcomes in both events A and B.

The union of events A and B ( AB ) is the set of all outcomes in either event A or B.