his presentation explains the concept of sampling, its purpose, and the main types of sampling methods. It focuses on probability and non-probability sampling techniques with simple explanations and examples.

1. Introduction to
Sampling Methods
Dr. P. VETRI SELVI
Assistant Professor
Department of Statistics
The Madura College (Autonomous)
Madurai
December 2025

2. Outline
What is Sampling?
The Purpose of Sampling
Types of Sampling Method

3. What is Sampling?
Sampling is the process of selecting a subset of
individuals from a population to estimate characteristics
of the whole population.

4. The Purpose of Sampling
The purpose of sampling is to study a representative
subset of a larger population to draw statistically valid
conclusions about the entire group, which is often
impractical to study in its entirety due to constraints
like cost, time, and complexity.

5. Types of Sampling Method
Sampling techniques are categorized into two main
types

6. Probability Sampling and
Non -Probability Sampling
The Probability sampling is defined as a sampling
technique in which the researcher chooses samples
from a larger population using a method based on the
theory of probability.
The non-probability sampling method is a technique in
which the researcher selects the sample based on
subjective judgment rather than the random selection

7. Probability Sampling
Simple Random Sampling
 Applicable when population is small, homogeneous
and readily available
 All subsets of the frame are given an equal
probability. Each element of the frame thus has an
equal probability of selection.
 It provides for greatest number of possible samples.
This is done by assigning a number to each unit in the
sampling frame.
 A table of random number or lottery system is used
to determine which units are to be selected.

8. Simple Random Sampling – With
and Without Replacement
The sampling units are chosen with replacement(WR) because
the selected units are placed back in the population. The
sampling units are chosen without replacement (WOR) because
the units, once chosen, are not placed back in the population.
For example, if we catch fish, measure them, and immediately
return them to the water before continuing with the sample,
this is a WR design, because we might end up catching and
measuring the same fish more than once. However, if we do not
return the fish to the water, this becomes a WOR design.

9. Stratified Random Sampling
The population is divided into two or more groups
called strata, according to some criterion, such as
geographic location, grade level, age, or income, and
subsamples are randomly selected from each strata.

10. Cluster Sampling
 Cluster Sampling is an example of 'two-stage sampling' .
 First stage a sample of areas is chosen;
 Second stage a sample of respondents within those areas is
selected.
 Population divided into clusters of homogeneous units,
usually based on geographical contiguity.
 Sampling units are groups rather than individuals.
 A sample of such clusters is then selected.
 All units from the selected clusters are studied.
.

11. Cluster Sampling
Cluster Sampling is a type of sampling where an entire
population is first divided into clusters or groups. Then, a
random cluster is selected, from which data is collected,
instead of collecting data from all the individuals from the
entire population.
.

12. Cluster Sampling
Systematic sampling is a probability sampling method where
you select items from an ordered population list at a regular,
fixed interval (k), starting with a random point, ensuring a
structured and evenly spread sample, like picking every 10th
person from a list after a random start. I

13. stratified sampling vs cluster sampling

14. Non -Probability Sampling
Convenience sampling
In a convenience sampling method, the samples are
selected from the population directly because they are
conveniently available for the researcher.
The process of including whoever happens to be available
at the time
For example, if high school students
are conducting a study on the average
pizza consumption in the cafeteria
each week, they could call their
classmates and ask how many slices
they consume during the week

15. Judgmental sampling or Purposive sampling
The researcher chooses the sample based on who they think
would be appropriate for the study. This is used primarily
when there is a limited number of people that have
expertise in the area being researched
One example of judgemental sampling
is if a researcher wants to study the
buying patterns of high-end luxury car
owners. The researcher may use
judgemental sampling to select a
sample of individuals who they believe
are most likely to purchase a luxury
car.

16. Quota sampling
Quota sampling is a non-probability research method where
you divide a population into subgroups (quotas) based on
characteristics like age, gender, or location, then select
participants non-randomly from each group until you meet
predetermined numbers (quotas) for each, ensuring your
sample mirrors the population's proportions for those traits

17. Snowball Sampling
Snowball sampling is a non-probability sampling technique in
which the researcher selects the first few respondents
intentionally, and then those respondents help to identify
additional respondents.
Each selected respondent “recruits” or “refers” the next
participant — just like a snowball grows bigger as it rolls.
A study on street musicians:
•Researcher finds one street musician at a
railway station.
•He refers his two friends who also perform in
other locations.
•Those two refer more musicians.
•The sample expands through their social
network.

18. Thank you