Probability deals with the analysis of random phenomena. It is a way of assigning every event a value between zero and one, with the requirement that the event made up of all possible results is assigned a value of one.

**2. Experiment**

An operation which results in some well-defined outcomes is called an experiment.

**2.1. Random Experiment**

An
experiment whose outcome cannot be predicted with certainty is called a random
experiment. In other words, if an experiment is performed many times under
similar conditions and the outcome of each time is not the same, then this
experiment is called a random experiment.

**Example:**

**A).**Tossing of a fair coin

**B).**Throwing of an unbiased die

**C).**Drawing of a card from a well shuffled pack of 52 playing cards

**3. Sample Space**

The set of all possible outcomes of a random experiment is called the sample space for that experiment. It is usually denoted by S.

**Example:**

**A).**When a die is thrown, any one of the numbers 1, 2, 3, 4, 5, 6 can come up.

Therefore, sample space:

S = {1, 2, 3, 4, 5, 6}

**B).**When a coin is tossed either a head or tail will come up, then the sample space w.r.t. the tossing of the coin is:

S = {H, T}

**C).**When two coins are tossed, then the sample space is

**3.1 Sample Point or Event Point**

Each
element of the sample spaces is called a sample point or an event point.

**Example:**

When a die is thrown, the sample space is S = {1, 2, 3, 4, 5, 6} where 1, 2, 3, 4, 5 and 6 are the sample points.

**3.2 Discrete Sample Space**

A
sample space S is called a discrete sample if S is a finite set.

**4. Event**

A subset of the sample space is called an event.

**4.1. Problem of Events**

Sample
space S plays the same role as universal set for all problems related to the
particular experiment.

**(i).**ϕ is also the subset of S and is an impossible Event.

**(ii).**S is also a subset of S which is called a sure event or a certain event.

**5. Types of Events**

A. Simple Event or Elementary Event

A. Simple Event or Elementary Event

An
event is called a Simple Event if it is a singleton subset of the sample space
S.

**Example:**

**A)**. When a coin is tossed, then the sample space is

S = {H, T}

Then A = {H} occurrence of head and

B = {T} occurrence of tail are called Simple events.

**B).**When two coins are tossed, then the sample space is

S = {(H,H); (H,T); (T,H); (T,T)}

Then
A = {(H,T)} is the occurrence of head on 1st and tail on 2nd is
called a Simple event.

**B. Mixed Event or Compound Event or Composite Event**

A
subset of the sample space S which contains more than one element is called a
mixed event or when two or more events occur together, their joint occurrence
is called a

**Compound Event**.**Example:**

When
a dice is thrown, then the sample space is

S = {1, 2, 3, 4, 5, 6}

S = {1, 2, 3, 4, 5, 6}

Then
let A = {2, 4 6} is the event of occurrence of even and B = {1, 2, 4} is the
event of occurrence of exponent of 2 are Mixed events.

Compound events are of two type:

Compound events are of two type:

**(i).**Independent Events, and

**(ii).**Dependent Events

**C. Equally likely events**

Outcomes
are said to be equally likely when we have no reason to believe that one is
more likely to occur than the other.

**Example:**

When
an unbiased die is thrown all the six faces 1, 2, 3, 4, 5, 6 are equally likely
to come up.

**D. Exhaustive Events**

A
set of events is said to be exhaustive if one of them must necessarily happen
every time the experiments is performed.

**Example:**

When
a die is thrown events 1, 2, 3, 4, 5, 6 form an exhaustive set of events.

**Important:**We can say that the total number of elementary events of a random experiment is called the exhaustive number of cases.

**E. Mutually Exclusive Events**

Two or more events are said to be mutually exclusive if one of them occurs, others cannot occur. Thus if two or more events are said to be mutually exclusive, if not two of them can occur together.

Hence,
A1,A2,A3,…,An are mutually exclusive if and only if Ai∩Aj=ϕ, for i≠j

**Example:**

**A).**When a coin is tossed the event of occurrence of a head and the event of occurrence of a tail are mutually exclusive events because we cannot have both head and tail at the same time.

**B).**When a die is thrown, the sample space is S = {1, 2, 3, 4, 5, 6}

Let A is an event of occurrence of number greater than 4 i.e., {5, 6}

B is an event of occurrence of an odd number {1, 3, 5}

C is an event of occurrence of an even number {2, 4, 6}

Here, events B and C are Mutually Exclusive but the event A and B or A and C are not Mutually Exclusive.

**F. Independent Events or Mutually Independent events**

Two
or more event are said to be independent if occurrence or non-occurrence of any
of them does not affect the probability of occurrence of or non-occurrence of
their events.

Thus, two or more events are said to be independent if occurrence or non-occurrence of any of them does not influence the occurrence or non-occurrence of the other events.

Thus, two or more events are said to be independent if occurrence or non-occurrence of any of them does not influence the occurrence or non-occurrence of the other events.

**Example:**

Let
bag contains 3 Red and 2 Black balls. Two balls are drawn one by one

**with replacement**.
Let
A is the event of occurrence of a red ball in first draw.

B is the event of occurrence of a black ball in second draw.

B is the event of occurrence of a black ball in second draw.

Then
probability of occurrence of B has not been affected if A occurs before B. As
the ball has been replaced in the bag and once again we have to select one ball
out of 5(3R + 2B) given balls for event B.

**6. Occurrence of an Event**

For a random experiment, let E be an event

Let E = {a, b, c}. If the outcome of the experiment is either a or b or c then we say the event has occurred.

**Sample Space:**The outcomes of any type

**Event:**The outcomes of particular type

**6.1. Probability of Occurrence of an event**

Let
S be the same space, then the probability of occurrence of an event E is
denoted by P(E) and is defined as

P(E)=n(E)n(S)= number of elements in E number of elements in S

P(E)= number of favourable/particular cases total number of cases

P(E)=n(E)n(S)= number of elements in E number of elements in S

P(E)= number of favourable/particular cases total number of cases

**Example:**

**A).**When a coin is tossed, then the sample space is S = {H, T}

Let E is the event of occurrence of a head

E = {H}

**B).**When a die is tossed, sample space S = {1, 2, 3, 4, 5, 6}

Let A is an event of occurrence of an odd number

And B is an event of occurrence of a number greater than 4

A
= {1, 3, 5} and B = {5, 6}

P(A)
= Probability of occurrence of an odd number =n(A)n(S)

=36=12 and

P(B) = Probability of occurrence of a number greater than 4 =n(B)n(S)

=26=13

=36=12 and

P(B) = Probability of occurrence of a number greater than 4 =n(B)n(S)

=26=13

**7. Basic Axioms of Probability**

Let S denote the sample space of a random experiment.

1. For any event E, P(E)≥0

2. P(S)=1

3. For a finite or infinite sequence of disjoint events E1,E2,…

P(E1∪E2∪E3∪…)=∑iP(Ei)