Sampling is the process of selecting a subset of individuals from within a population to estimate characteristics of the whole population. It is done because examining the entire population would be impossible or impractical in terms of time, cost, and effort. There are two main types of sampling: probability sampling, where every member of the population has a chance of being selected; and non-probability sampling, which does not use random selection. Some common sampling techniques include simple random sampling, stratified random sampling, cluster sampling, and systematic random sampling. Issues to consider with sampling include sampling error and sampling bias.

1. - Anurag Kumar Deb
Roll no. 6

2. What is “Sampling”?
Source:Internet

3. Sampling is defined as the selection of
some part of an aggregate or totality on the
basis of which a judgement or inference
about the aggregate or totality is made.
It is the process of obtaining information
about an entire population by examining
only a part of it.
The process of selecting population from
the sample is called sampling.

4. Why Sampling?
Sampling is done usually because it is impossible
to test every single individual in the population. It
is also done to save time, money and effort while
conducting the research.

5. Advantages: Disadvantages:
 Time Saving.
 Economical
 Accuracy
 Covers large Size of
population.
 Biasedness.
 Need for Specialised
Knowledge.
 Selection of true
representative sample.

6. Sampling Types:
Probability Sample: Non Probability Sample:
 It is a method of Random
sampling where all
individuals in the population
have an equal chance of being
selected.
 It is a method where random
selection and method are not
based on the rationale of
probability theory.

7. Types

8. Simple Random Sampling:
 Simple random Sampling is a technique of probability
sampling where all the population has an equal chance
of being selected.
 In this sampling the selection is free from bias.
 Eg. Suppose a company like Amul want to check the
response of his product(e.g. Amul Lassi) in the market
, then they can go with this technique. Let say they
visited our university and there are 3000 students
from which they select 300 students randomly by a
table of randomly generated numbers.

9. Systematic random Sampling:
 Systematic random Sampling is a technique where
there is a gap between each selected unit in the
sample. It follows a calculation to be very précised.
 E.g.: In our MBA department ,student strength is 50
and Professor want a sample of 5 group then
50/5=10.Then our Professor will be taking every 10th
student from the group. For instance in this case they
are Roll no. 10,20,30,40,50.

10. Stratified Random Sampling:
 Stratified Random Sampling is a technique where the
population is firstly divided into strata's and a sample
is selected from each stratum.
 The subgroups that has been made should have similar
characteristics.
 Eg. If we are making strata’s in our MBA department
then it can be of (Male And Female),(B.Sc, B.Tech,
B.Com ,BCA,BBA) etc and picking samples from each
strata's then it is Stratified Sampling.

11. Cluster Sampling:
 Cluster Sampling is the process of randomly selecting
“Intact Groups”, not individuals, within the defined
population sharing similar characteristics.
 Clusters are location within which an intact group of
members of the population can be found.
 It is one of the best technique to use when populations
are large and spread over a large Geographic Region.
 Examples:
Neighbourhood, schools,classrooms

12. Source:Internet

13. Quota Random Sampling:
 It is a technique where the sample frame is already
decided and the sample is just selected to meet these
characteristics.
 Eg. While doing MBA there comes an offer by TATA
Group that they will be hiring 40% Male and 60%
Female for a particular job (total vacancy is 10 i.e. 4
Male and 6 Female , but in our department there are
50 students so the random selection should be such
that which meets the above mentioned criteria.

14. Multi Stage Random Sampling:
 In simple words it is a complex form of Cluster
Sampling where we use a variety of Sampling methods
together.
 Sampling Scheme that combines several methods are
called Multi Stage Random Sampling.
 This sampling technique is more accurate than cluster
sampling for same sample size because the sample gets
more filtration over stages.

16. Types

17. Convenience Sampling:
 A convenience Sampling is simply one where the units
are selected for inclusion in the sample are easiest to
access.
 In simple words, it’s a non probability sampling tool
which picks up the sample that are close to hand.
 E.g.: Suppose we are asked to visit shops and to do
research on smokers and non smokers of Silchar and if
we visit shops ,only near to my house then it will fall
under Convenience sampling.

18. Snowball Sampling:
 This technique is particularly appropriate when the
population you are interested in is hidden and/or hard
to reach.
 These include populations such as drug addict,
homeless people, criminals etc.
 E.g.: Suppose in CID, the senior officer gets a trace
about the criminal and he asks his subordinate to join
the link and catch the other criminal associated with
the case. This type of sampling is snowball sampling.

19. Important Issues:
Sampling Error:
 The chance occurrence that a randomly selected
Sample is not a representative of the population due
to errors inherent in sampling technique.
 Sampling error is maximum when one take small
sample size.
 Small Sample size- Greater Sampling Error
Big Sample Size- Lesser chance of Sampling Error.

20. Sampling Bias:
In statistics, sampling bias is a bias in which a sample
is collected in such a way that some members of the
intended population have a lower sampling
probability than others. It results in a biased sample, a
non-random sample of a population (or non-human
factors) in which all individuals, or instances, were not
equally likely to have been selected.