If probability of occurrence of an event A is not affected by occurrence of another event B, then A and B are said to be independent events.

Consider an example of rolling a die. If A is the event ‘the number appearing is odd’ and B be the event ‘the number appearing is a multiple of 3’, then

P(A)= \( \frac 36 \)

Also A and B is the event ‘the number appearing is odd and a multiple of 3’ so that

P(A ∩ B) = \( \frac 16 \)

P(A│B) = \( \frac {P(A ∩ B)}{ P(B)} \)

= \( \frac {\frac 16}{\frac 13 } \)

P(A) = P(A│B) = \( \frac 12 \)

If A and B are independent events , then P(A│B) = P(A)

Using Multiplication rule of probability, P(A ∩ B) = P(B) .P(A│B)

P(A ∩ B) = P(B) .P(A)

NOTE: A and B are two events associated with the same random experiment, then A and B are known as independent events if P(A ∩ B) = P(B) .P(A)

What are mutually exclusive events?

Two events A and B are said to be mutually exclusive events if they cannot occur at the same time. Mutually exclusive events never have an outcome in common.

### Differences between independent events and mutually exclusive events:

Independent Events | Mutually exclusive events |

They cannot be specified based on the outcome of a maiden trial. | They are independent of trials |

Can have common outcomes | Can never have common outcomes |

If A and B are two independent events, then
P(A ∩ B) = P(B) .P(A) |
If A and B are two mutually exclusive events, then
P(A ∩ B) = 0 |

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