Definitions of Probability:MUTUALLY EXCLUSIVE EVENTS, Venn Diagram

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MTH001 Elementary Mathematics

LECTURE # 30

Definitions of Probability:

· Subjective Approach to Probability

· Objective Approach:

· Classical Definition of Probability

Relative Frequency Definition of Probability

Before we begin the various definitions of probability, let us revise the concepts of:

· Mutually Exclusive Events

· Exhaustive Events

· Equally Likely Events

MUTUALLY EXCLUSIVE EVENTS:

Two events A and B of a single experiment are said to be mutually exclusive or disjoint if

and only if they cannot both occur at the same time i.e. they have no points in common.

EXAMPLE-1:

When we toss a coin, we get either a head or a tail, but not both at the same time.

The two events head and tail are therefore mutually exclusive.

EXAMPLE-2:

When a die is rolled, the events `even number' and `odd number' are mutually exclusive as

we can get either an even number or an odd number in one throw, not both at the same

time. Similarly, a student either qualifies or fails, a person is either a teenager or not a

teenager, etc., etc.

Three or more events originating from the same experiment are mutually exclusive if

pair wise they are mutually exclusive.

If the two events can occur at the same time, they are not mutually exclusive, e.g., if we

draw a card from an ordinary deck of 52 playing cars, it can be both a king and a diamond.

Therefore, kings and diamonds are not mutually exclusive. Speaking of playing

cards, it is to be remembered that an ordinary deck of playing cards contains 52 cards

arranged in 4 suits of 13 each. The four suits are called diamonds, hearts, clubs and

spades; the first two are red and the last two are black. The face values called

denominations, of the 13 cards in each suit are ace, 2, 3, ..., 10, jack, queen and king. The

face values called denominations, of the 13 cards in each suit are ace, 2, 3, ..., 10, jack,

queen and king.

We have discussed the concepts of mutually exclusive events.

Another important concept is that of exhaustive events.

EXHAUSTIVE EVENTS:

Events are said to be collectively exhaustive, when the union of mutually

exclusive events is equal to the entire sample space S.

EXAMPLES:

1. In the coin-tossing experiment, `head' and `tail' are collectively exhaustive events.

2. In the die-tossing experiment, `even number' and `odd number' are collectively exhaustive

events.

In conformity with what was discussed in the last lecture:

PARTITION OF THE SAMPLE SPACE:

A group of mutually exclusive and exhaustive events belonging to a sample space is

called a partition of the sample space. With reference to any sample space S, events A and

⎯A form a partition as they are mutually exclusive and their union is the entire sample space.

The Venn D

iagram below clearly indicates this point.

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Venn Diagram

⎯A is shaded

Next, we consider the concept of equally likely events:

EQUALLY LIKELY EVENTS:

Two events A and B are said to be equally likely, when one event is as likely to occur

as the other.

In other words, each event should occur in equal number in repeated trials.

EXAMPLE:

When a fair coin is tossed, the head is as likely to appear as the tail, and the

proportion of times each side is expected to appear is 1/2.

EXAMPLE:

If a card is drawn out of a deck of well-shuffled cards, each card is equally likely to

be drawn, and the proportion of times each card can be expected to be drawn in a very

large number of draws is 1/52.Having discussed basic concepts related to probability theory,

we now begin the discussion of THE CONCEPT AND DEFINITIONS OF PROBABILITY.

Probability can be discussed from two points of view: the subjective approach, and the

objective approach.

SUBJECTIVE OR PERSONALISTIC PROBABILITY:

As its name suggests, the subjective or personalistic probability is a measure of the

strength of a person's belief regarding the occurrence of an event A. Probability in this

sense is purely subjective, and is based on whatever evidence is available to the individual.

It has a disadvantage that two or more persons faced with the same evidence may arrive at

different probabilities.

For example, suppose that a panel of three judges is hearing a trial. It is possible

that, based on the evidence that is presented, two of them arrive at the conclusion that the

accused is guilty while one of them decides that the evidence is NOT strong enough to draw

this conclusion.

On the other hand, objective probability relates to those situations where everyone will

arrive at the same conclusion.

It can be classified into two broad categories, each of which is briefly described as follows:

1. The Classical or `A Priori' Definition of Probability

If a random experiment can produce n mutually exclusive and equally likely

outcomes, and if m out to these outcomes are considered favorable to the occurrence of a

certain event A, then the probability of the event A, denoted by P(A), is defined as the ratio

m/n.

Symbolically, we write

P(A ) =

Number of favourable outcomes

Total number of possible outcomes

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This definition was formulated by the French mathematician P.S. Laplace (1949-1827) and

can be very conveniently used in experiments where the total number of possible outcomes

and the number of outcomes favourable to an event can be DETERMINED.

Let us now consider a few examples to illustrate the classical definition of probability:

EXAMPLE-1:

If a card is drawn from an ordinary deck of 52 playing cards, find the probability that

i) the card is a red card, ii) the card is a 10.

SOLUTION :

The total number of possible outcomes is 13+13+13+13 = 52, and we assume that all

possible outcomes are equally likely.(It is well-known that an ordinary deck of cards contains

13 cards of diamonds, 13 cards of hearts, 13 cards of clubs, and 13 cards of spades.)

(i) Let A represent the event that the card drawn is a red card.

Then the number of outcomes favourable to the event A is 26 (since the 13 cards of

diamonds and the 13 cards of hearts are red).

He nce

P(A ) =

Number of favourable outcomes

Total number of possible outcomes

26 1

52 2

P(B) =

= .

Thus

EXAMPLE-2:

A fair coin is tossed three times. Wha52 th13 robability that at least one head appears?

t is e p

SOLUTION:

The sample space for this experiment is

{HHH, HHT, HTH, THH,

HTT, THT, TTH, TTT}

and thus the total number of sample points is 8

i.e. n(S) = 8.Let A denote the event that at least one head appears. Then

{HHH, HHT, HTH,

THH, HTT, THT, TTH}

and therefore n(A) = 7.

He nce

n(A ) 7

P(A ) =

= .

n(S) 8

EXAMPLE-3:

Four items are taken at random from a box of 12 items and inspected. The box is rejected if

more than 1 item is found to be faulty. If there are 3 faulty items in the box, find the

probability that the box is accepted.

SOLUTION:

⎛12 ⎞

⎜ ⎟ = 495

⎜ 4⎟

⎝ ⎠

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The sample space S contains

sample points

⎛12 ⎞

⎜ ⎟

(because there are ⎜ 4 ⎟

⎝ ⎠

ways of selecting four items out of twelve).

The box contains 3 faulty and 9 good items. The box is accepted if there is (i) no faulty

items, or (ii) one faulty item in the sample of 4 items selected.

Let A denote the event the number of faulty items chosen is 0 or 1.

Then

⎛ 3 ⎞ ⎛ 9 ⎞ ⎛ 3⎞ ⎛ 9 ⎞

n( A)

= ⎜ ⎟ ⎜ ⎟ + ⎜ ⎟⎜ ⎟

⎜ 0 ⎟ ⎜ 4 ⎟ ⎜1 ⎟ ⎜ 3 ⎟

⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎝ ⎠

= 126 + 252 = 378 sample po int s.

m 378

P(A ) =

∴

= 0.76

n 495

Hence

the

probability

that

the

box

accepted

76%

(in spite of the fact that the box contains 3 faulty items).

The classical definition has the following shortcomings:

i) This definition is said to involve circular reasoning as the term equally likely really means

equally probable.

Thus probability is defined by introducing concepts that presume a prior knowledge of the

meaning of probability.

ii) This definition becomes vague when the possible outcomes are INFINITE in number, or

uncountable.

iii) This definition is NOT applicable when the assumption of equally likely does not hold.

And the fact of the matter is that there are NUMEROUS situations where the assumption of

equally likely cannot hold.

And these are the situations where we have to look for another definition of probability!

The other definition of probability under the objective approach is the relative

frequency definition of probability.

The essence of this definition is that if an experiment is repeated a large number of

times under (more or less) identical conditions, and if the event of our interest occurs a

certain number of times, then the proportion in which this event occurs is regarded as the

probability of that event.

For example, we know that a large number of students sit for the matric examination

every year. Also, we know that a certain proportion of these students will obtain the first

division, a certain proportion will obtain the second division, --- and a certain proportion of

the students will fail.

Since the total number of students appearing for the matric exam is very large, hence:

· The proportion of students who obtain the first division --- this proportion can be

regarded as the probability of obtaining the first division,

· The proportion of students who obtain the second division --- this proportion can be

regarded as the probability of obtaining the second division, and so on.

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