This document discusses sampling and different sampling techniques. It defines sampling as selecting a subset of individuals from a population to gather data and make conclusions about the larger population. 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, where not every member has a known chance of selection. Some common sampling techniques discussed are simple random sampling, systematic sampling, stratified sampling, cluster sampling, convenience sampling, judgment sampling, quota sampling, and snowball sampling. The document also distinguishes between random sampling error and non-sampling error.

1. The process of selecting a number of individuals
for a study in such a way that the individuals
represent the larger group from which they were
selected
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2. 2
•To gather data about the population in order to make
an conclusion that can be generalized to the population.
•In order to carry out primary research it is necessary
to use sampling.

3. 3
Identify your target population
Determine Sampling Frame
Determine Sampling Procedure
( Technique to be used )
Determine Sample Size
Execute Sampling Design

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Techniques Of Sampling
Probability Sampling Non-Probability Sampling
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Convenience Sampling
Judgment Sampling
Quota Sampling
Snowball Sampling

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Probability Sampling
Simple Random Sampling
 Here There is an Equal Probability of any Element to
Receive the Treatment among the Population.
 Applicable when population is small, homogeneous &
readily available
 Thus, It is Also Called Chance Sampling Or
Probability Sampling.
 For Example Drawing lots( Lottery Method ) or Using
random number tables( Tippet’s Method )

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Probability Sampling
Systematic Sampling
 In Some Instances the most practical way of sampling is
to select every 15th name on a list, every 10th house on
one side of a street and so on.
 An Element of Randomness is usually introduced into
this kind of sampling by using random numbers to pick
up the unit with which to start.
 This procedure is useful when sampling frame is
available in the form of a list

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Probability Sampling
Stratified Sampling
 If the population from which a sample is to be drawn
does not constitute a homogeneous group then it is useful.
In this technique, the population is stratified into a
number of non-overlapping sub-populations or strata and
sample items are selected from each stratum.

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Probability Sampling
Cluster Sampling
 It is useful when it would be impossible or
impractical to identify every person in the sample.
Population is divided into groups,
geographic area

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Non-Probability Sampling
Convenience Sampling
The process of including whoever happens to be available at the
time is called “accidental” or “haphazard” sampling.
For Example - If a Researcher wishes to secure data from, say,
Gasoline Buyers, he may select a fixed number of petrol
stations and may conduct interviews at these stations. This
would be an example of convenience sample of gasoline
buyers.

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Non-Probability Sampling
Judgment Sampling
The sample is selected based upon judgment.
- an extension of convenience sampling.
The researcher chooses the sample based on who they think
would be appropriate for the study.
For Example - A Judgment sample of college students might be
taken to secure reactions to a new method of teaching

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Non-Probability Sampling
Quota Sampling
Quota sampling is the non-probability equivalent of stratified
sampling.
•First identify the stratums and their proportions as they are
represented in the population
•Then convenience or judgment sampling is used to select the
required number of subjects from each stratum.

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Non-Probability Sampling
Snowball Sampling
It is when you don't know the best people to study because of the
unfamiliarity of the topic or the complexity of events. So you ask
participants during interviews to suggest other individuals to be
sampled.

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 Random error- the sample selected is not
representative of the population due to chance.
 The level of it is controlled by sample size.
 A larger sample size leads to a smaller sampling
error.
Sampling Error
Random Sampling Error

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Sampling Error
 Systematic Error
 The level of it is not controlled by sample size.
Non-sampling Error

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