Probability Class 11 Notes Maths Chapter 16

CBSE Class 11 Maths Notes Chapter 16 Probability

Random Experiment
An experiment whose outcomes cannot be predicted or determined in advance is called a random experiment.

Outcome
A possible result of a random experiment is called its outcome.

Sample Space
A sample space is the set of all possible outcomes of an experiment.

Events
An event is a subset of a sample space associated with a random experiment.

Types of Events
Impossible and sure events: The empty set Φ and the sample space S describes events. Intact Φ is called the impossible event and S i.e. whole sample space is called sure event.

Simple or elementary event: Each outcome of a random experiment is called an elementary event.

Compound events: If an event has more than one outcome is called compound events.

Complementary events: Given an event A, the complement of A is the event consisting of all sample space outcomes that do not correspond to the occurrence of A.

Mutually Exclusive Events
Two events A and B of a sample space S are mutually exclusive if the occurrence of any one of them excludes the occurrence of the other event. Hence, the two events A and B cannot occur simultaneously and thus P(A ∩ B) = 0.

Exhaustive Events
If E₁, E₂,…….., E_n are n events of a sample space S and if E₁ ∪ E₂ ∪ E₃ ∪………. ∪ E_n = S, then E₁, E₂,……… E₃ are called exhaustive events.

Mutually Exclusive and Exhaustive Events
If E₁, E₂,…… E_n are n events of a sample space S and if
E_i ∩ E_j = Φ for every i ≠ j i.e. E_i and E_j are pairwise disjoint and E₁ ∪ E₂ ∪ E₃ ∪………. ∪ E_n = S, then the events
E₁, E₂,………, E_n are called mutually exclusive and exhaustive events.

Probability Function
Let S = (w₁, w₂,…… w_n) be the sample space associated with a random experiment. Then, a function p which assigns every event A ⊂ S to a unique non-negative real number P(A) is called the probability function.
It follows the axioms hold

• 0 ≤ P(w_i) ≤ 1 for each W_i ∈ S
• P(S) = 1 i.e. P(w₁) + P(w₂) + P(w₃) + … + P(w_n) = 1
• P(A) = ΣP(w_i) for any event A containing elementary event w_i.

Probability of an Event
If there are n elementary events associated with a random experiment and m of them are favorable to an event A, then the probability of occurrence of A is defined as

The odd in favour of occurrence of the event A are defined by m : (n – m).
The odd against the occurrence of A are defined by n – m : m.
The probability of non-occurrence of A is given by P(\(\bar { A }\)) = 1 – P(A).

Addition Rule of Probabilities
If A and B are two events associated with a random experiment, then
P(A ∪ B) = P(A) + P(B) – P(A ∩ B)
Similarly, for three events A, B, and C, we have
P(A ∪ B ∪ C) = P(A) + P(B) + P(C) – P(A ∩ B) – P(A ∩ C) – P(B ∩ C) + P(A ∩ B ∩ C)

Note: If A and B are mutually exclusive events, then
P(A ∪ B) = P(A) + P(B)