This document provides an overview of sampling techniques used in research. It defines key terms like population, target population, sampling, and elements. It also describes different sampling methods like probability sampling (simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, sequential sampling) and non-probability sampling (purposive sampling, convenient sampling, consecutive sampling, quota sampling, snowball sampling). The document explains the steps involved in the sampling process and factors to consider for good sampling. It highlights the merits and demerits of different sampling methods.

1. UNIT-5
SAMPLING
PREPARED BY
MRS.AKILA .A, M.Sc (N), M.Sc (PSY)
ASSOCIATE PROFESSOR

2. INTRODUCTION
• Sampling is a complex and technical topic.
Yet at the same time, sampling is familiar to
us all. In the course of our daily activities, we
get information, make decisions and develop
predictions through sampling. We all come to
conclusions about phenomena based on
exposure to a limited portion of those
phenomena.
• Example: Patients may generalise about
nurse’s friendliness in a particular hospital
based on the care they received from a
sample of nurses.

3. Terminologies
POPULATION: Population is the entire aggregation of all the
units in which a researcher is interested. In other words,
population is the set of people or entire to which the results of a
research are to be generalized.
For example, a researcher needs to study the complications of
diabetes Mellitus among Patients with diabetes Mellitus in
kumbakonam; in this the ‘population’ will be all the diabetic
Patients who are in kumbakonam.

4. TARGET POPULATION: A target population consist
of the total number of people or objects which are
meeting the designated set of criteria. In other
words, it is the aggregate of all the cases with a
certain phenomenon about which the researcher
would like to make a generalization.
For example, a researcher is interested
in identifying the complication of diabetes mellitus
type-II.

6. • SOURCE POPULATION: The population
from which the study subjects are drawn.
• STUDY SAMPLE/GROUP: Consists of
the individuals (animals or groups of
animals) that end up in the study

7. ACCESSIBLE POPULATION: It is the aggregate of cases that
confirm to designated criteria & are also accessible as subjects
for a study.
For example: ‘a researcher is conducting a study on the
diabetic Mellitus patients who are residing in Kumbakonam. In
this case, the population for this study is all the patients with
diabetes Mellitus who are in kumbakonam , but some of them
may be out of station & may not be accessible for research
study. Therefore, accessible population for this study will be
diabetic mellitus patients who meet the designated criteria &
who are also available for the research study.

9. SAMPLING:
Sampling is the process of selecting a portion of
the population to represent the entire population.
SAMPLE:
Sample is a subset of population elements. An
element is the most basic unit about which information
is collected.
Sampling helps the researcher in
• Reduces the time and cost of research studies
• It saves labour
• The quality will be better and gives quicker results
• It can save time and money

10. Element: The individual entities that
comprise the samples & population are
known as elements, & an element is the
most basic unit about whom/which
information is collected.
• An elements is also known as subject in
research. The most common element in
nursing research is an individual.

11. • Sampling frame: It is a list of all the elements or
subjects in the population from which the sample
is drawn. Sampling frame could be prepared by
the researcher or an existing frame may be used..
• Sampling error: There may be fluctuation in the
values of the statistics of characteristics from one
sample to another, or even those drawn from the
same population.
• Sampling bias: Distortion that arises when a
sample is not representative of the population from
which it was drawn.
• Sampling plan: The formal plan specifying a
sampling method, a sample size, & the procedure
of selecting the subjects.

12. SAMPLING CRITERIA:
• Sampling criteria is the list of characteristics of the
elements that we have determined beforehand that
are essential for eligibility to form part of the sample.
• We determine what these criteria (or essential
characteristics) are as a result of the research problem
or the purpose of the research.
Example:
• Age
• Gender
• Marital status
• Ethnic status
• Type of disease that they may have
• Type of treatment
• Ability to understand particular language
• Ability to write and etc....

13. INCLUSION CRITERIA are characteristics
that the prospective subjects must have if
they are to be included in the study.
• EXCLUSION CRITERIA are those
characteristics that disqualify prospective
subjects from inclusion in the study.

14. SAMPLING ERROR
There may be a fluctuations in the values of
the statistics of characteristics from one sample
to another, or even those drawn from the same
population
SAMPLING BIAS
Systematic overrepresentation or
underrepresentation of some segment of the
population in terms of a characteristics relevant to the
research question.

17. Sampling process

18. PURPOSES OF SAMPLING
 Economical: In most cases, it is not possible &
economical for researchers to study an entire
population. With the help of sampling, the researcher
can save lots of time, money, & resources to study a
phenomenon.
 Improved quality of data: It is a proven fact that when
a person handles less amount the work of fewer number
of people, then it is easier to ensure the quality of the
outcome.

19. • Quick study results: Studying an entire population itself will
take a lot of time, & generating research results of a large
mass will be almost impossible as most research studies
have time limits.
• Precision and accuracy of data: Conducting a study on
provides researchers with voluminous data, & maintaining
precision of that data
• To gather data about the population in order to make an
inference that can be generalized to the population

20. CHARACTERISTICS OF GOOD
SAMPLE
 Representative
 Free from bias and errors
 No substitution and incompleteness
 Appropriate sample size
 Accessible
 Low cost

21. FACTORS INFLEUNCING
SAMPLING:
Nature of the researcher:
• Inexperienced investigator
• Lack of interest
• Lack of honesty
• Intensive workload
• Inadequate supervision

22. Nature of the sample:
• Inappropriate sampling technique
• Sample size
• Defective sampling frame
Circumstances :
• Lack of time
• Large geographic area
• Lack of cooperation
• Natural calamities

23. STEPS IN SAMPLING PROCESS:
Define the population
Identify the sampling frame
Specify the sampling unit
Specify the sampling method
Determining the sample size
Specifying the sampling plan
Selecting a desired sample

24. 1.Identifying and defining the target
population
• First step of sampling process
• It consists of the group of people or objects
which are meeting the designated set of criteria
and researcher would like to generalise the
information

25. 2.Describing the accessible population
and ensuring sampling frame
• Researcher establish a complete description
about accessible population and
• prepare a complete list of subjects to select a
sample with the help of sampling frame.

26. 3.Specifying the sampling unit
• Researcher must establish the specific inclusion
and exclusion criteria in order to eliminate the
confusion while selection of the sample

27. 4.Specifying the sample selection
methods
• Researcher decides whether sample will be
drawn from the population by using probability
and nonprobability methods.

28. 5.Determining the sample size
• It is very essential so that the researcher can
plan the implementation of the sampling process
accordingly

29. 6.Specifying sampling plan
• Before selection of particular sample, the researcher
must have a final sampling plan, so that sampling
process can be implemented without any undue
problems.

30. 7.Selecting a desired sample
• Finally researcher draw a representative sample
from the accessible population, which requires
the implementation plan of sampling process
and selecting a desired sample for data
collection.

31. TYPES OF SAMPLING
PROBABILITY NON PROBABILITY
1. Simple random sampling
2. Stratified random sampling
3. Systematic random
sampling
4. Cluster/multistage
sampling
5. Sequential sampling
1. Purposive sampling
2. Convenient sampling
3. Consecutive sampling
4.Quota sampling
5.Snow ball sampling

32. PROBABILITY SAMPLING
• It is based on the theory of probability.
• It involve random selection of the
elements/members of the population.
• In this, every subject in a population has
equal chance to be selected sampling for a
study.
• In probability sampling techniques, the
chances of systematic bias is relatively less
because subjects are randomly selected.

33. FEATURES OF PROBABILITY SAMPLING:
• It is a technique wherein the sample are gathered
in a process that given all the individuals in the
population equal chances of being selected.
• In this sampling technique, the researcher must
guarantee that every individual has an equal
opportunity for selection.
• The advantage of using a random sample is the
absence of both systematic & sampling bias.
• The effect of this is a minimal or absent systematic
bias, which is a difference between the results
from the sample & those from the population.

34. Simple random sampling
• This is the most pure & basic probability sampling
design.
• In this type of sampling design, every member of
population has an equal chance of being selected
as subject.
• The entire process of sampling is done in a single
step, with each subject selected independently of
the other members of the population
• There is need of two essential prerequisites to
implement the simple random technique:
population must be homogeneous & researcher
must have list of the elements/members of the
accessible population.

35. • Example: Evaluate the prevalence of tooth decay
among the 1200 children attending a school
– List of children attending the school
– Children numerated from 1 to 1200
– Sample size = 100 children
– Random sampling of 100 numbers between 1
and 1200

36. The lottery method…
• It is most primitive & mechanical method.
• Each member of the population is assigned a
unique number.
• Each number is placed in a bowel or hat & mixed
thoroughly.
• The blind-folded researcher then picks numbered
tags from the hat.
• All the individuals bearing the numbers picked by
the researcher are the subjects for the study.

37. The use of table of random
numbers…
• This is most commonly & accurately used method
in simple random sampling.
• Random table present several numbers in rows &
columns.
• Researcher initially prepare a numbered list of the
members of the population, & then with a blindfold
chooses a number from the random table.
• The same procedure is continued until the desired
number of the subject is achieved.
• If repeatedly similar numbers are encountered,
they are ignored & next numbers are considered
until desired numbers of the subject are achieved.

38. The use of computer…
• Nowadays random tables may be generated
from the computer , & subjects may be
selected as described in the use of random
table.
• For populations with a small number of
members, it is advisable to use the first
method, but if the population has many
members, a computer-aided random
selection is preferred.

40. Merits
• Ease of assembling the sample
• Fair way of selecting a sample
• Require minimum knowledge about the
population in advance
• It unbiased probability method
• Free from sampling errors

41. Demerits
• It requirement of a complete & up-to-date
list of all the members of the population.
• Does not make use of knowledge about a
population which researchers may already
have.
• Lots of procedure need to be done before
sampling
• Expensive & time-consuming

42. Stratified random sampling
• First divide the population into two or more
strata
• Aim is to enhance representativeness of the
sample
• Subdivide the population into homogeneous
subsets from which appropriate numbers of
subjects are selected at random.
• Stratification is often based on demographic
attributes: age, gender, income level.

43. Stratum A B C
Population size 100 200 300
Sample fraction 1/2 1/2 1/2
Final sample size 50 100 150

44. Stratum A B C
Population size 100 200 300
Sampling fraction 1/2 1/4 1/6
Final sample size 50 50 50

45. Merits:
• Increase probability of sample being
representative
• Assure adequate number of cases for
subgroups
Demerits:
•Requires adequate knowledge and population
•Costly to prepare stratified list
•Statistics are more complicated

46. Systematic Random Sampling
• It can be likened to an arithmetic progression,
wherein the difference between any two
consecutive numbers is the same.
• It involves the selection of every Kth case from list
of group, such as every 10th person on a patient
list or every 100th person from a phone directory.
• Systematic sampling is sometimes used to sample
every Kth person entering a bookstore, or passing
down the street or leaving a hospital & so forth
• Systematic sampling can be applied so that an
essentially random sample is drawn.

47. • If we had a list of subjects or sampling frame, the
following procedure could be adopted. The desired
sample size is established at some number (n) &
the size of population must know or estimated (N).
Number of subjects in target population (N)
K = N/n or K=
Size of sample (n)
• For example, a researcher wants to choose about
100 subjects from a total target population of 500
people. Therefore, 500/100=5. Therefore, every 5th
person will be selected.

48. • For example, there are 10,000 elements in
the population and a sample of 100 is
desired.
• In this case the sampling interval = N/n
10000/100= 10
A random number between 1 and 10 is
selected.
If, for example, this number is 3
sample consists of elements 13, 23, 33, 43, 53
etc

49. Merits
• Convenient & simple to carry out.
• Distribution of sample is spread evenly
over the entire given population.
• Less cumbersome, time-consuming, &
cheaper

50. Demerits:
• If first subject is not randomly selected, then it
becomes a nonrandom sampling technique
• Sometimes this may result in biased sample.
• If sampling frame has non randomly, this
sampling technique may not be appropriate
to select a representative sample.

51. Cluster sampling
• It is done when simple random sampling is almost
impossible because of the size of the population.
• Cluster sampling means random selection of sampling
unit consisting of population elements.
• Then from each selected sampling unit, a sample of
population elements is drawn by either simple random
selection or stratified random sampling.
• This method is used in cases where the population
elements are scattered over a wide area, & it is
impossible to obtain a list of all the elements.
• The important thing to remember about this sampling
technique is to give all the clusters equal chances of
being selected.

52. Types of Cluster Sampling
– One stage
– Two stage
– Multi stage
Sample successively selected by units as
states – cities- districts – villages - households
multistage sampling described in terms of number of
stages ( eg. Three stage cluster sampling)

53. • Geographical units are the most commonly used
ones in research. For example, a researcher
wants to survey academic performance of high
school students in India.
• He can divide the entire population (of India) into
different clusters (cities).
Then the researcher selects a number of clusters
depending on his research through simple or
systematic random sampling.
• Then, from the selected clusters (random selected
cities), the researcher can either include all the
high school students as subjects or he can select
a number of subjects from each cluster through
simple or systematic sampling.

54. Merits
• It cheap, quick, & easy for a large
population.
• Large population can be studied, & require
only list of the members.
• Investigators to use existing division such
as district, village/town, etc.
• Same sample can be used again for study

55. Demerits
• This technique is the least representative
of the population.
• Possibility of high sampling error
• This technique is not at all useful.

56. Sequential sampling
• This method of sample selection is slightly
different from other methods.
• Here the sample size is not fixed. The
investigator initially selects small sample &
tries out to make inferences; if not able to
draw results, he or she then adds more
subjects until clear-cut inferences can be
drawn.

57. Merits:
• Study on best possible smallest sample
• Facilitates inferences of study
Demerits:
• Not possible to study a phenomenon
• Requires the repeated entry into field to
collect the sample

58. NON PROBABILITY SAMPLING
• It is a technique wherein the samples are
gathered in a process that does not given
all the individuals in the population equal
chances of being selected in the sample.
• In other words, in this type of sampling
every subject does not have equal chance
to be selected because elements are
chosen by choice not by chance through
nonrandom sampling methods.

59. Features of Non Probability
Sampling
• It is a technique wherein samples are gathered in a
process that does not give all the individual in the
population equal chances of being selected.
• Most researchers are bound by time, money, &
workforce, & because of these limitations, it is almost
impossible to randomly sample the entire population &
it is often necessary to employ another sampling
technique, the non probability sampling technique.
• Subject in a non probability sample are usually
selected on the basis of their accessibility or by the
purposive personal judgment of the researcher.

60. Uses of Non probability
Sampling
• This type of sampling can be used when demonstrating that a
particular trait exists in the population.
• It can also be used when researcher aims to do a qualitative,
pilot , or exploratory study.
• It can be used when randomization is not possible like when
the population is almost limitless.
• it can be used when the research does not aim to generate
results that will be used to create generalizations.
• It is also useful when the researcher has limited budget, time,
& workforce.
• This technique can also be used in an initial study (pilot study)

61. Purposive Sampling
• It is more commonly known as ‘judgmental’ or
‘authoritative sampling’.
• In this type of sampling, subjects are chosen to be
part of the sample with a specific purpose in mind.
• In purposive sampling, the researcher believes
that some subjects are fit for research compared
to other individual. This is the reason why they are
purposively chosen as subject.
• In this sampling technique, samples are chosen by
choice not by chance, through a judgment made
the researcher based on his or her knowledge
about the population

62. • For example, a researcher wants to study the lived
experiences of post disaster depression among people
living in earthquake affected areas of Gujarat.
• In this case, a purposive sampling technique is used to
select the subjects who were the victims of the
earthquake disaster & have suffered postdisaster
depression living in earthquake-affected areas of
Gujarat.
• In this study, the researcher selected only those
people who fulfill the criteria as well as particular
subjects that are the typical & representative part of
population as per the knowledge of the researcher.

63. Merits:
• Simple to draw sample & useful in explorative
studies
• Save resources, require less fieldwork.
Demerits:
• Require considerable knowledge about the
population under study.
• It is not always reliable sample, as conscious
biases may exist.
• Two main weakness of purposive sampling are
with the authority & in the sampling process.
• It is usually biased since no randomization was
used to obtained the sample.

64. Convenience Sampling
• It is probably the most common of all sampling
techniques because it is fast, inexpensive, easy, & the
subject are readily available.
• It is a non probability sampling technique where
subjects are selected because of their convenient
accessibility & proximity to the researcher.
• The subjects are selected just because they are
easiest to recruit for the study & the researcher did not
consider selecting subjects that are representative of
the entire population
• It is also known as an accidental sampling.
• Subjects are chosen simply because they are easy to
recruit.

65. • For example, if a researcher wants to conduct
a study on the older people residing in
kumbakonam & the researcher observes that
he can meet several older people coming for
morning walk in a park located near his
residence in kumbakonam, he can choose
these people as his research subjects.
• These subjects are readily accessible for the
researcher & may help him to save time,
money, & resources.

66. Merits
• This technique is considered easiest,
cheapest, & least time consuming.
• This sampling technique may help in saving
time, money, & resources.
Demerits:
• Sampling bias, & the sample is not
representative of the entire population.
• It does not provide the representative sample
from the population of the study.
• Findings generated from these sampling
cannot be generalized on the population.

67. Consecutive Sampling
• It is very similar to convenience sampling except that it seeks
to include all accessible subjects as part of the sample.
• This non probability sampling technique can be considered as
the best of all non probability samples because it include all
the subjects that are available, which makes the sample a
better representation of the entire population.
• It is also known as total enumerative sampling.

68. • In this sampling technique, the investigator pick up
all the available subjects who are meeting the
preset inclusion & exclusion criteria.
• This technique is generally used in small-sized
populations.
• For example, if a researcher wants to study the
activity pattern of postkidney-transplant patient, he
can selects all the postkideney transplant patients
who meet the designed inclusion & exclusion
criteria, & who are admitted in post-transplant
ward during a specific time period.

69. Merits:
• Little effort for sampling
• It is not expensive, not time consuming, & not
workforce intensive.
• Ensures more representativeness of the selected
sample.
Demerits
• Researcher has not set plans about the sample
size & sampling schedule.
• It always does not guarantee the selection of
representative sample.
• Results from this sampling technique cannot be
used to create conclusions & interpretations
pertaining to the entire population.

70. Quota Sampling
• It is non probability sampling technique wherein
the researcher ensures equal or proportionate
representation of subjects, depending on which
trait is considered as the basis of the quota.
• The bases of the quota are usually age, gender,
education, race, religion, & socio-economic status.
• For example, if the basis of the quota is college
level & the research needs equal representation,
with a sample size of 100, he must select 25 first-
year students, another 25 second-year students,
25 third-year, & 25 fourth-year students.

71. Merits
• Economically cheap, as there is no need to
approach all the candidates.
• Suitable for studies where the fieldwork has to be
carried out, like studies related to market & public
opinion polls.
Demerits
• It not represent all population
• In the process of sampling these subgroups, other
traits in the sample may be overrepresented.
• Not possible to estimate errors.
• Bias is possible, as investigator/interviewer can
select persons known to him.

72. Snow ball Sampling
• It is a non probability sampling technique that is
used by researchers to identify potential subjects
in studies where subjects are hard to locate such
as commercial sex workers, drug abusers, etc.
• For example, a researcher wants to conduct a
study on the prevalence of HIV/AIDS among
commercial sex workers.
• In this situation, snowball sampling is the best
choice for such studies to select a sample.
• This type of sampling technique works like chain
referral. Therefore it is also known as chain
referral sampling.

73. • After observing the initial subject, the
researcher asks for assistance from the
subject to help in identify people with a
similar trait of interest
• The process of snowball sampling is much
like asking subjects to nominate another
person with the same trait.
• The researcher then observes the nominated
subjects & continues in the same way until
obtaining sufficient number of subjects.

74. Merits :
• The chain referral process allows the researcher
to reach populations that are difficult to sample
when using other sampling methods.
• The process is cheap, simple, & cost-efficient.
• Need little planning & lesser workforce
Demerits:
• Researcher has little control over the sampling
method.
• Representativeness of the sample is not
guaranteed.
• Sampling bias is also a fear of researchers when
using this sampling technique.

75. Technique Strength Weakness
Probability
Simple Random
Sampling
Easily understood, results
projectable
Expensive, assurance of
representative
Stratified sampling Includes all important sub
populations
Expensive, Difficult to
select relevant
stratification variables
Systemic sampling Increases
representativenes
Representative
ness changes
Cluster sampling Easy to implement, cost
effective
Difficult to interpret
results
Non probability
Convenience sampling Least expensive, least
time consuming.
Quota sampling Sample can be controlled
for certain characteristics
Bias, no assurance
of
representativeness

76. Choosing non Probability vs. Probability sampling
Conditions favouring the use of
Factors Non probability
sampling
Probability sampling
Nature of research Exploratory Conclusive
Relative magnitude
of sampling and
non sampling
errors
Non sampling errors
are larger
Sampling errors are
larger
Variability in
the population
Homogeneous Heterogeneous
Statistical
consideration
Unfavourable Favourable
Operational
considerations
Favourable Unfavourable

77. SAMPLING IN
QUALITATIVE RESEARCH
• Researchers in qualitative research select
their participants according to their :
1) Characteristics
2) Knowledge

78. The purposeful sampling
• It is when the researcher chooses persons or sites
which provide specific knowledge about the topic
of the study.
1) Maximal Variation Sampling
2) Typical Sampling
3) Theory or Concept Sampling
4) Homogeneous Sampling
5) Critical Sampling
6) Opportunistic Sampling
7) Snowball Sampling

79. Maximal Variation Sampling :
• It is when you select individuals that differ on a certain
characteristic. In this strategy you should first identify
the characteristic and then find individuals or sites which
display that characteristic.
Typical Sampling:
• It is when you study a person or a site that is “typical” to
those unfamiliar with the situation. You can select a
typical sample by collecting demographic data or survey
data about all cases.
Theory or Concept Sampling:
It is when you select individuals or sites because they
can help you to generate a theory or specific concepts
within the theory. In this strategy you need a full
understanding of the concept or the theory expected to
discover during the study.

80. Homogeneous Sampling:
It is when you select certain sites or
people because they possess similar
characteristics. In this strategy, you need
to identify the characteristics and find
individuals or sites that possess it.
Critical Sampling:
It is when you study an exceptional
case represents the central
phenomenon in dramatic terms.

81. Opportunistic Sampling:
It is used after data collection begins,
when you may find that you need to
collect new information to answer your
research questions.
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.

82. PROBLEMS OF SAMPLING
 Sampling errors
 Lack of sample representativeness
 Difficulty in estimation of sample size
 Lack of knowledge about the sampling
process
 Lack of resources
 Lack of cooperation
 Lack of existing appropriate sampling
frames for larger population

83. Errors
Sampling
Errors
Biased
errors
Unbiased
errors
Non Sampling
Errors

84. SAMPLING ERROR
• It is the deviation of the selected sample
from the true characteristics, traits,
behaviours, qualities or figures of the
entire population.

85. CAUSES OF SAMPLING ERROR
 Individual difference of subjects: Researchers
draw different subjects from the same population
but still the subjects have individual difference. A
sample is only, a subset of the entire population.
Therefore, there may be a difference between the
sample and the population.
 Biased sampling procedure:It is a tendency to
favour a selection of sample units that posses a
particular characteristics. It may occur in the form of
over and under-representation bias.

86. • Chance error :
The error occurs by chance. e.g. – Someone
did a comparative study on mal-nutrition on
under five children in two cities A and B.
Unfortunately if city B had a large number of
slum dwellers, so, this will comprise a large
number of mal-nourished children than city-A
which is going to give a skewed result.
A process of randomization and probability
sampling is done to minimize such kind of
sampling errors but it is still possible that all
the randomized subjects are not the
representative of the population

87. Difficulty in estimation of sample size:
• There is lack of availability of population
parameters; in the absence of population
parameters it becomes very difficult to estimate
the sample size. Studies conducted on
undetermined sample size do not facilitate to
generalize the findings for the particular
population.
Lack of knowledge about the
sampling process
• If the researcher is having superficial knowledge
about the sampling methods and sampling
process then this will lead to poor selection and
ultimately affects the study results

88. Lack of re-sources:
Resource allocation for nursing research studies are
very poor in developing country like India. So in the
absence of appropriate resources drawing a good
representative sample is very difficult and biased too
Lack of cooperation:
• Sometimes study subjects are not co-operative with
researcher and after selection of samples by means of
simple random sampling they are not willing to
participate in the study, at this time, then it will create a
problem for the researcher in sampling.

89. OVER-COMING STRATEGIES
• There is only one way to eliminate this kind of error...that
to eliminate the concepts of sample and to test the
entire population, which is really not possible, so, one
has to adapt then... a following strategies for
overcoming these types of problems...
These are :
• Use of proper and un-biased probability sampling
• Avoid convenient or judgmental sampling.
• Ensure that the target population is well defined and the
sample frame should match it as much as possible.
• Increasing the size of sample.
• Inclusion and exclusioncriteria

90. Increasing the size of
sample :
SAMPLING ERROR SAMPLING ERROR
sample sample

91. NON SAMPLING ERRORS
• A non-sampling error is a statistical term that refers
to an error that results during data collection,
causing the data to differ from the true values.
• A non-sampling error differs from a sampling error.
In general, the sampling errors decrease as the
sample size increases, whereas non-sampling
error increases as the sample size increases.

92. CAUSES
i) Lack of trained and qualified investigators
ii) Due to wrong answers to the questions
iii) Due to incomplete coverage
iv) Biasness of the investigators
v) Vague questionnaire
vi) Faulty list of population
vii) Wrong method of asking questions
viii) Wrong calculations while processing the data
ix) Failure of respondent memory to recall past events
x)Error while printing the results