![]() |
Generated on 9/15/2025 at 7:59 PM |
The webpage this document was printed/exported from can be found at the following URL:
https://www.cpalms.org//PreviewStandard/Preview/15764
https://www.cpalms.org//PreviewStandard/Preview/15764
Calculate the conditional probability of two events and interpret the result in terms of its context.
Standard #: MA.912.DP.4.3
Standard Information
General Information
Subject Area: Mathematics (B.E.S.T.)
Grade: 912
Strand: Data Analysis and Probability
Date Adopted or Revised: 08/20
Status: State Board Approved
Standard Instructional Guide
Connecting Benchmarks/Horizontal Alignment
Terms from the K-12 Glossary
- Event
- Sample space
Vertical Alignment
Previous Benchmarks
Next Benchmarks
Purpose and Instructional Strategies
In middle grades, students began working with theoretical probabilities and comparing them to experimental probability. In Algebra I students determined frequencies from two-way tables. In Math for College Liberal Arts, students calculate the conditional probability of two events and interpret the results in context of the problem.- Conditional probability of an event is the probability that the event will occur given the knowledge that another event has already occurred.
- Conditional probability is written as P(B|A) and is read as the probability of event B given event A.
- Instruction includes understanding that the probability of event A given event B is
different than the probability of event A given event B. Students should note that the
formulas for both events are different.
The conditional probability of P(B |A) = .
- For example, Jackson is rolling a fair six-sided die. He wants to find the
probability that the number rolled is a five, given that it is odd.
The sample space for this experiment is the set S = {1, 2, 3, 4, 5, 6} consisting of
six equally likely outcomes. F denotes the event “a five is rolled” and O denotes the event “an odd number is
rolled.” F = {5} and O = {1, 3, 5}
P(F |O) =
F ∩ O = {5} ∩ {1,3, 5}; so F ∩ O = {5}
P(F ∩ O) =
Since O = {1, 3, 5}, P(O) = or
P(F |O) = = =
- For example, Jackson is rolling a fair six-sided die. He wants to find the
probability that the number rolled is a five, given that it is odd.
The sample space for this experiment is the set S = {1, 2, 3, 4, 5, 6} consisting of
six equally likely outcomes. F denotes the event “a five is rolled” and O denotes the event “an odd number is
rolled.”
- Informally, conditional probability of P(B |A) can be thought of as restricting the sample
space to only include the outcomes in event A.
- For example, Jackson is rolling a fair six-sided die. He wants to find the
probability that the number rolled is a five, given that it is odd.
We can reduce our sample space to the odd numbers only.O = {1, 3, 5}
One of these outcomes is a 5, therefore P(rolling a 5 | Odd) = .
The conditional probability of P(A|B) = . - For example, Jackson is rolling a fair six-sided die. He wants to find the
probability that the number rolled is an odd, given that it is five.
The sample size for this experiment is the set S = 1, 2, 3, 4, 5, 6 consisting of six
equally likely outcomes. F denotes the event “a five is rolled” and O denotes the event “an odd number is
rolled.”F = 5 and O = 1, 3, 5
P(O |F) =
O ∩ F = 1,3,5 ∩ 5; so O ∩ F = 5
P(O ∩ F) =
Since F = 5, P(F) =
P(O |F) = = = 1
- For example, Jackson is rolling a fair six-sided die. He wants to find the
probability that the number rolled is a five, given that it is odd.
We can reduce our sample space to the odd numbers only.
- Informally, conditional probability of P(A|B) can be thought of as restricting the sample
space to only include the outcomes in event B.
- For example, Jackson is rolling a fair six-sided die. He wants to find the probability that the number rolled is a odd, given that it is five. We can reduce our sample space to the number 5.
- This one outcome is odd, therefore P(Odd | rolling a 5) = = 1.
- Conditional probabilities can be observed in tree diagrams as the branch stemming from another branch which may be a helpful visual for some students to understand the meaning of conditional probabilities.
- Instruction includes the use of two way tables.
- It is not the expectation for this benchmark for students to memorize formulas.
Common Misconceptions or Errors
- Students may think the symbol used for conditional probability is a slash that would be used to represent division and simply divide the probability of A by the probability of B.
- Students may get confused as to which event probability should be the denominator.
- Students may get confused when working with a two-way table that they need to restrict
their answer to a certain section that is from the “given” conditional piece.
- For example, when given the condition of male they are only looking in the row or column containing males to get the total.
- Students who have difficulty with the terminology and notation will also have difficulty in understanding what is being asked by the questions.
Instructional Tasks
Instructional Task 1 (MTR.7.1)- All the students at All Florida High School were surveyed and they were classified according
to year and whether or not they have one or both ears pierced. One student is randomly
selected.
- Part A. Are the events of having your ears pierced and the year you are in school independent or dependent? How do you know?
- Part B. What is the probability of the selected student having pierced ears?
- Part C. What is the probability of the selected student having pierced ears given that the student is a sophomore?
- Part D. What is the probability of the selected student having pierced ears given that the student is not a sophomore?
- Part E. What is the probability of the selected student being a sophomore given they have pierced ears?
Instructional Items
Instructional Item 1- Compute the following probabilities in connection with the roll of a single fair six-sided die.
- Part A. The probability that the roll is even.
- Part B. The probability that the roll is even, given that it is not a four.
- Part C. The probability that the roll is even, given that it is not a three.
Instructional Item 2
- A special deck of 16 cards has 4 that are green, 4 yellow, 4 blue, and 4 red. The four cards of
each color are numbered from one to four. A single card is drawn at random. Find the
following probabilities.
- Part A. The probability that the card drawn is red.
- Part B. The probability that the card drawn is red, given that it is not blue.
- Part C. The probability that the card drawn is red, given that it is not a four.
Related Courses
- Algebra 2 Honors (Specifically in versions: 2014 - 2015, 2015 - 2022, 2022 - 2024, 2024 and beyond (current)) # 1200340
- Probability and Statistics Honors (Specifically in versions: 2014 - 2015, 2015 - 2019, 2019 - 2022, 2022 - 2024, 2024 and beyond (current)) # 1210300
- Access Mathematics for Liberal Arts (Specifically in versions: 2014 - 2015, 2015 - 2018, 2018 - 2019, 2019 - 2022, 2022 - 2023, 2023 and beyond (current)) # 7912070
- Mathematics for College Statistics (Specifically in versions: 2022 - 2024, 2024 and beyond (current)) # 1210305
- Mathematics for College Liberal Arts (Specifically in versions: 2022 - 2024, 2024 and beyond (current)) # 1207350
Related Access Points
- MA.912.DP.4.AP.3 # Given the probability of two events, P(A and B) and P(A), in decimal form, select the conditional probability of the two events {[P(A and B))/(P(A)]}.
Related Resources
Lesson Plans
- Casino Royale # Students examine games of chance to determine the difference between dependent and independent conditional probability.
-
How to Hit it Big in the Lottery - Probability of Compound Events # Students will explore a wide variety of interesting situations involving probability of compound events. Students will learn about independent and dependent events and their related probabilities.
Lesson includes:
- Bell-work that reviews prerequisite knowledge
- Directions for a great In-Your-Seat Game that serves as an interest builder/introduction
- Vocabulary
- Built-in Kagan Engagement ideas
- An actual lottery activity for real-life application
- Tree Diagrams and Probability # This lesson is designed to develop students' ability to create tree diagrams and figure probabilities of events based on those diagrams. This lesson provides links to discussions and activities related to tree diagrams as well as suggested ways to work them into the lesson. Finally, the lesson provides links to follow-up lessons designed for use in succession with the current one.
- Modeling Conditional Probabilities 1: Lucky Dip # This lesson unit is intended to help you assess how well students are able to understand conditional probability, represent events as a subset of a sample space using tables and tree diagrams, and communicate their reasoning clearly.
Problem-Solving Tasks
- Rain and Lightning # This problem solving task challenges students to determine if two weather events are independent, and use that conclusion to find the probability of having similar weather events under certain conditions.
- Lucky Envelopes # Students answer questions about the probabilities of independent and dependent events.
- Cards and Independence # This problem solving task lets students explore the concept of independence of events.
- How Do You Get to School? # This task requires students to use information in a two-way table to calculate a probability and a conditional probability.
- The Titanic 2 # This task lets students explore the concepts of probability as a fraction of outcomes using two-way tables.
- The Titanic 1 # This task asks students to calculate probabilities using information presented in a two-way frequency table.
- The Titanic 3 # This problem solving task asks students to determine probabilities and draw conclusions about the survival rates on the Titanic using a table of data.
Text Resource
- The Logic of Drug Testing # This informational text resource is intended to support reading in the content area. This article explores the reliability of drug tests for athletes, using mathematics. The author attempts to address this issue by relating drug tests to conditional probability. Throughout the text, various numbers that affect the calculation of a reliable probability are discussed. Numbers such as test sensitivity, test specificity, and weight of evidence are related to Bayes' theorem, which is ultimately used to calculate the conditional probability.