
Addition Rule For Probabilities
The addition rule for probabilities describes two formulas, one for the probability for either of two mutually exclusive events happening and the other for the probability of two non-mutually exclusive events happening. Mathematically, the probability of two non-mutually exclusive events is denoted by: P ( Y or Z ) \= P ( Y ) \+ P ( Z ) − P ( Y and Z ) P(Y \\text{ or } Z) = P(Y) + P(Z) - P(Y \\text{ and } Z) P(Y or Z)\=P(Y)+P(Z)−P(Y and Z) To illustrate the first rule in the addition rule for probabilities, consider a die with six sides and the chances of rolling either a 3 or a 6. Mathematically, the probability of two mutually exclusive events is denoted by: P ( Y or Z ) \= P ( Y ) \+ P ( Z ) P(Y \\text{ or } Z) = P(Y)+P(Z) P(Y or Z)\=P(Y)+P(Z) The addition rule for probabilities describes two formulas, one for the probability for either of two mutually exclusive events happening and the other for the probability of two non-mutually exclusive events happening. Non-mutually-exclusive means that some overlap exists between the two events in question and the formula compensates for this by subtracting the probability of the overlap, P(Y and Z), from the sum of the probabilities of Y and Z.

What Is the Addition Rule for Probabilities?
The addition rule for probabilities describes two formulas, one for the probability for either of two mutually exclusive events happening and the other for the probability of two non-mutually exclusive events happening.
The first formula is just the sum of the probabilities of the two events. The second formula is the sum of the probabilities of the two events minus the probability that both will occur.



The Formulas for the Addition Rules for Probabilities Is
Mathematically, the probability of two mutually exclusive events is denoted by:
P ( Y or Z ) = P ( Y ) + P ( Z ) P(Y \text{ or } Z) = P(Y)+P(Z) P(Y or Z)=P(Y)+P(Z)
Mathematically, the probability of two non-mutually exclusive events is denoted by:
P ( Y or Z ) = P ( Y ) + P ( Z ) − P ( Y and Z ) P(Y \text{ or } Z) = P(Y) + P(Z) - P(Y \text{ and } Z) P(Y or Z)=P(Y)+P(Z)−P(Y and Z)
What Does the Addition Rule for Probabilities Tell You?
To illustrate the first rule in the addition rule for probabilities, consider a die with six sides and the chances of rolling either a 3 or a 6. Since the chances of rolling a 3 are 1 in 6 and the chances of rolling a 6 are also 1 in 6, the chance of rolling either a 3 or a 6 is:
1/6 + 1/6 = 2/6 = 1/3
To illustrate the second rule, consider a class in which there are 9 boys and 11 girls. At the end of the term, 5 girls and 4 boys receive a grade of B. If a student is selected by chance, what are the odds that the student will be either a girl or a B student? Since the chances of selecting a girl are 11 in 20, the chances of selecting a B student are 9 in 20 and the chances of selecting a girl who is a B student are 5/20, the chances of picking a girl or a B student are:
11/20 + 9/20 - 5/20 =15/20 = 3/4
In reality, the two rules simplify to just one rule, the second one. That's because in the first case, the probability of two mutually exclusive events both happening is 0. In the example with the die, it's impossible to roll both a 3 and a 6 on one roll of a single die. So the two events are mutually exclusive.
Mutual Exclusivity
Mutually exclusive is a statistical term describing two or more events that cannot coincide. It is commonly used to describe a situation where the occurrence of one outcome supersedes the other. For a basic example, consider the rolling of dice. You cannot roll both a five and a three simultaneously on a single die. Furthermore, getting a three on an initial roll has no impact on whether or not a subsequent roll yields a five. All rolls of a die are independent events.
Related terms:
Compound Probability
Compound probability is a mathematical term relating to the likeliness of two independent events occurring. read more
Conditional Probability
Conditional probability is the chances of an event or outcome that is itself based on the occurrence of some other previous event or outcome. read more
Joint Probability
Joint probability is a statistical measure that calculates the likelihood of two events occurring together and at the same point in time. Joint probability is the probability of event Y occurring at the same time that event X occurs. read more
Mutually Exclusive
Mutually exclusive is a statistical term describing two or more events that cannot occur simultaneously. read more
Objective Probability
Objective probability is the probability that an event will occur based on an analysis in which each measurement is based on a recorded observation. read more
Random Variable
A random variable is a variable whose value is unknown, or a function that assigns values to each of an experiment's outcomes. read more
T-Test
A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. read more
Uniform Distribution
Uniform distribution is a type of probability distribution in which all outcomes are equally likely. Learn how to calculate uniform distribution. read more