Watch
Watching this resources will notify you when proposed changes or new versions are created so you can keep track of improvements that have been made.
Favorite
Favoriting this resource allows you to save it in the “My Resources” tab of your account. There, you can easily access this resource later when you’re ready to customize it or assign it to your students.
The Multiplication Rule
The multiplication rule states that the probability that A and B occur is equal to the probability that A occurs times the probability that B occurs.
Learning Objective

Apply the multiplication rule to the probability A and B occurring
Key Points
 The multiplication rule is understood given that we know A has already occurred.
 The multiplication rule can be written as: P(A∩B) = P(B) x P(AB).
 The events A and B are defined on a sample space, which is the set of all possible outcomes of an experiment.
 We obtain the general multiplication rule by multiplying both sides of the definition of conditional probability by the denominator.
Terms

sample space
The set of all possible outcomes of a game, experiment or other situation.

multiplication rule
The probability that A and B occur is equal to the probability that A occurs times the probability that B occurs, given that we know A has already occurred.
Full Text
In probability theory, The Multiplication Rule states that the probability that A and B occur is equal to the probability that A occurs times the probability that B occurs, given that we know A has already occurred. This rule can be written:
P(A∩B) = P(B) x P(AB),
or alternatively as:
P(A∩B) = P(A) x P(BA).
As illustrated in , the events A and B are defined on a sample space, which is the set of all possible outcomes of an experiment.
We obtain the general multiplication rule by multiplying both sides of the definition of conditional probability by the denominator. In general, the conditional probability can be defined as follows:
If P(B) ≠ 0 then the conditional probability of A relative to B is given by
P(AB) = P(A∩B) x P(B).
If P(AB) = P(A) then we say that A and B are independent events.
As an example, suppose that we draw two cards out of a deck of cards and let A = {first card is an ace}, and B = {second card is an ace}, then
and
The denominator in the latter equation is 51 since we know a card has been drawn already. Therefore, there are 51 left in total. We also know the first card was an ace, therefore:
P(A∩B) = P(A) x P(BA),
P(A∩B) =
Assign just this concept or entire chapters to your class for free.
Key Term Reference
 conditional probability
 Appears in these related concepts: The Exponential Distribution, Bayes' Rule, and The Collins Case
 experiment
 Appears in these related concepts: Experimental Design, Primary Market Research, and Descriptive and Correlational Statistics
 independent
 Appears in these related concepts: Conditional Probability, Unions and Intersections, and Party Identification
 independent event
 Appears in these related concepts: The Paradox of the Chevalier De Méré, Independence, and Basic Concepts
 probability
 Appears in these related concepts: Particle in a Box, The Addition Rule, and Rules of Probability for Mendelian Inheritance
 probability theory
 Appears in these related concepts: Applications of Statistics, Expected Value and Standard Error, and Independence
 sample
 Appears in these related concepts: Sampling, Defining the Sample and Collecting Data, and Basic Inferential Statistics
Sources
Boundless vets and curates highquality, openly licensed content from around the Internet. This particular resource used the following sources:
Cite This Source
Source: Boundless. “The Multiplication Rule.” Boundless Statistics. Boundless, 21 Jul. 2015. Retrieved 30 Nov. 2015 from https://www.boundless.com/statistics/textbooks/boundlessstatisticstextbook/probability8/probabilityrules34/themultiplicationrule1714445/