This document discusses different types of non-random sampling methods, including judgment sampling, quota sampling, and convenience sampling. Judgment sampling relies on the investigator's personal judgment to select samples, quota sampling divides the population into subgroups and fixes quotas for samples from each, and convenience sampling prioritizes the investigator's convenience when selecting samples. The document provides examples and discusses the merits and demerits of each type of non-random sampling. It also outlines two laws of sampling: the law of statistical regularity and the law of inertia of large numbers.

1. ◤
NON RANDOM SAMPLING
Non-random sampling is one in which all the items of the universe
do not have equal chances of being selected.
The investigator selects samples on the basis of convenience or
his judgment rather than on the basis of probability.

2. ◤
TYPES OF NON RANDOM SAMPLING
▪ JUDGMENT SAMPLING
▪ QUOTA SAMPLING
▪ CONVENIENCE SAMPLING

3. ◤
JUDGMENT SAMPLING
▪ Under this method, the choice of sample items depends
exclusively on the judgment of the investigator.
● On the basis of his own choice, he tries to select the best
representative of the whole population.
● It is also known as purposive and deliberate sampling.

4. ◤
EXAMPLE
If a music teacher has to select five students from his/her school for
participation in an inter-school competition. He/She cannot use a
random sampling method.
In this case, he/she will use his/her own judgment to select those
five students from a big lot.

5. ◤
Merits:
It is useful where the personal judgment of the investigator is
important. It helps in drawing the sample of a small size.
It helps in observing some characteristics in detail.

6. ◤
Demerits :
It is not based on probability, it does not guarantee accuracy.
The selection of items may be affected by personal bias or
prejudice.

7. ◤
QUOTA SAMPLING
● Under this method, the items of the population are subdivided into
various groups and then a quota (number of items to be selected
from each sub-group) is fixed.
● However, within the given quota, the selection of sample units
depends upon the personal judgment of the investigator. So, this is
a type of judgment sampling only.

8. ◤
EXAMPLE
In a survey of Reliance Jio network users, the interviewers may be
told to interview 100 people living in a certain area.
Out of those 100, 60% of the interviewed are to be working people,
30% should be students, and others to be 10%.
Within these quotas, the interviewer is free to select the people to
be interviewed.

9. ◤
Merits:
It provides satisfactory results if quotas are allocated objectively.
Each part of the population gets representation.
Satisfactory results are expected.

10. ◤
Demerits :
This method is subjected to personal bias.
It proves useful only if the interviewers are properly trained.

11. ◤
CONVENIENCE SAMPLING
● Under this method, while selecting the sample units, the
investigator gives special attention to his convenience.
● This method of selecting the sample is also known as ‘chunk’

12. ◤
EXAMPLE
To estimate the average height of an Indian, the investigator
(belonging to Delhi) can take a convenience sample from Delhi
only and estimate the average height of an Indian.

13. ◤
Merits:
It is useful when the universe is not properly defined.
It is useful for the economy of time, money, and efforts.
Demerits :
The sample items may not truly represent the universe.
The results obtained are often less reliable.

14. ◤
LAWS OF SAMPLING
▪ Law of Statistical Regularity
▪ Law of Inertia of Large Numbers

15. 1. Law of Statistical Regularity
The law states that if a moderately large number of items are selected at random
from a given population, the characteristics of those items will reflect, to a fairly
accurate degree, the characteristic of the entire population.
2. Law of Inertia of Large Numbers
The law of inertia of large numbers is a corollary of the law of statistical
regularity and lays down that ‘in large masses of data abnormalities will occur,
but in all probability, exceptional items will offset each other, leaving the
average unchanged subject, where the element of time enters, to the general
trend of data.