Mutually Exclusive Events

In probability theory, two events are said to be mutually exclusive if they cannot occur at the same time or simultaneously. In other words, mutually exclusive events are called disjoint events. If two events are considered disjoint events, then the probability of both events occurring at the same time will be zero. If A and B are the two events, then the probability of probability is written by:

Probability of Disjoint (or) Mutually Exclusive Event = P ( A and B) = 0

In probability, the specific addition rule is valid when two events are mutually exclusive. It states that the probability of either event occurring is the sum of probabilities of each event occurring. If A and B are said to be mutually exclusive events then the probability of an event A occurring or the probability of event B occurring is given as P(A) + P(B), i.e.,

P (A or B) = P(A) + P(B)

Some of the examples of the mutually exclusive events are:

  • When tossing a coin, the event of getting head and tail are mutually exclusive. Because the probability of getting head and tail simultaneously is 0.
  • In a six-sided die, the events “2” and “5” are mutually exclusive. We cannot get both the events 2 and 5 at the same time when we threw one die.
  • In a deck of 52 cards, drawing a red card and drawing a club are mutually exclusive events because all the clubs are black.

If the events A and B are not mutually exclusive, the probability of getting A or B is given as

P (A or B) = P(A) + P(B) – P (A and B)

Dependent and Independent Events

Two events are said to be dependent if the occurrence of one event changes the probability of another event. Two events are said to be independent events if the probability of one event that does not affect the probability of another event. If two events are mutually exclusive, they are not independent and also independent events cannot be mutually exclusive.

Mutually Exclusive Events Probability Rules

In probability theory, two events are mutually exclusive or disjoint if they do not occur at the same time. A clear case is the set of results of a single coin toss, which can end in either heads or tails, but not for both. While tossing the coin, both outcomes are collectively exhaustive, which suggests that at least one of the consequences must happen, so these two possibilities collectively exhaust all the possibilities.

Though, not all mutually exclusive events are commonly exhaustive. For example, the outcomes 1 and 4 of a six-sided die, when we throw it, are mutually exclusive (both 1 and 4 cannot come as result at the same time) but not collectively exhaustive (it can result to distinct outcomes such as 2,3,5,6).

From the definition of mutually exclusive events, certain rules for the probability are concluded.

Addition Rule: P ( A + B ) = 1

Subtraction Rule: P ( A U B) = 0

Multiplication Rule: P ( A ∩ B ) = 0

There are different varieties of events also. For instance, think a coin that has a Head on both the sides of the coin or a Tail on both sides. It doesn’t matter how many times you flip it, it will always occur Head (for the first coin) and Tail (for the second coin). If we check the sample space of such experiment, it will be either { H } for the first coin and { T } for the second one. Such events have single point in the sample space and are called “Simple Events”. Such kind of two simple events are always mutually exclusive.

Conditional Probability for Mutually Exclusive Events

Conditional probability is stated as the probability of an event A, given that another event B has occurred. Conditional Probability for two independent events B has given A is denoted by the expression P( B|A) and it is defined using the equation

P(B|A)= P ( A ∩ B )/P(A)

Redefine the above equation using multiplication rule: P ( A∩B ) = 0

P(B|A)= 0/P(A)

So the conditional probability formula for mutually exclusive events is:

P ( B | A) = 0

Examples with Solutions

Here the sample problem for mutually exclusive events is given in detail. Go through once to learn easily.

Question 1: What is the probability of a die showing a number 3 or number 5?

Solution: Let,

P(3) is the probability of getting a number 3

P(5) is the probability of getting a number 5

P(3) = 1/6 and P(5) = 1/6


P( 3 or 5) = P(3) + P(5)

P ( 3 or 5) = (1/6) + (1/6) = 2/6

P( 3 or 5) = 1/3

Therefore, the probability of a die showing 3 or 5 is 1/3.

Question 2: Three coins are tossed at the same time. We say A as the event of receiving at least 2 heads. Likewise, B denotes the event of getting no heads and C is the event of getting heads on the second coin. Which of these is mutually exclusive?

Solution:  Firstly, let us create a sample space for each event. For the event ‘A’ we have to get at least two head. Therefore, we have to include all the events that have two or more heads.

Or we can write:


This set A has 4 elements or events in it i.e. n(A) = 4

In the same way,  for the event B, we can write the sample as:

B = { TTT } and n(B) = 1

Again using same logic, we can write;

C = { THT, HHH, HHT, THH } and n(C) = 4

So B & C and A & C are mutually exclusive since they have nothing in their intersection.