Population & sample lecture

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2. What is sampling ?
In simple words, sampling consists of obtaining
information from a portion of a larger group or
an universe. Elements are selected in a manner
that they yield almost all information about the
whole universe, if and when selected according
to some scientific principles and procedures.
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3. POPULATION

The entire aggregation of items from which
samples can be drawn is known as a
population.

In sampling, the population may refer to the
units, from which the sample is drawn.

A population of interest may be the universe of
nations or cities.

This is one of the first things the analyst needs
to define properly while conducting a business
research.

“N” represents the size of the population.
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4. CENSUS
A complete study of all the elements present in the
population is known as a census. The national
population census is an example of census survey
SAMPLE
A Sample is a selection of units from the entire group
called the population or universe of interest. It is
Subset of a larger population
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5. SAMPLE DESIGN
NON-PROBABILITY SAMPLES
PROBABILITY SAMPLES
.CONVENIENCE
● SIMPLE RANDOM
.JUDGMENTAL
● STRATIFIED
.QUOTA
● CLUSTER
.SNOWBALL
● SYSTEMATIC
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6. NON-PROBABILITY SAMPLING
The probability of any particular
member being chosen for the
sample is unknown.
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7. CONVENIENCE
SAMPLING

The sampling procedure of obtaining the people or
units that are most conveniently available

Accidental sampling is a type of non-probability
sampling which involves the sample being drawn
from that part of the population which is close to
hand
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8. QUOTA SAMPLING

in quota sampling, the population is first
segmented into mutually exclusive In quota
sampling the selection of the sample is nonrandom sub-groups

In the quota sampling the interviewers are
instructed to interview a specified no of persons
from each category. In studying peoples
status, living
conditions, preference, opinions, attitudes, etc
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9. JUDGEMENT SAMPLING

Samples in which the selection criteria are
based on personal judgment that the element is
representative of the population under study.
 Example:-In test marketing, a judgement is made as to
which cities would constitute the best ones for
testing the marketability of a new product.
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10. SNOWBALL SAMPLING

samples in which selection of additional
respondents is based on referrals from the initial
respondents

Initial respondents are selected by probability
methods

Additional respondents are obtained from
information provided by the initial respondents
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11. PROBABILITY SAMPLING
Every member of the population has a
known, non-zero probability of being selected
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12. Simple random sampling
Random sampling mean, the arrangement of conditions in
such a manner that every item of the whole universe
from which we are to select the sample shall have the
same chance of being selected as any other item.
Among all the probability sampling procedures random
sampling is the most basic and least complicated.
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13. Systematic sampling
1.
Prepare a list of all the elements in the universe and number
them. This list can be according to alphabetical order, as in
records etc.
2.
Then from the list, every third/every 8th / or any other
number in the like manner can be selected.
For this method, population needs to be homogeneous. This
method is frequently used, because it is simple, direct and
inexpensive. Also known as patterned, serial or chain
sampling.
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14. Stratified sampling

When the population is divided into
different stratas or groups and then
samples are selected from each
stratum by simple random sampling
procedure, we call it as stratified
random sampling
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15. Cluster Sampling

The whole population is divided in small
clusters it may be according to location. Then
clusters are selected in sample

The purpose of cluster sampling is to sample
economically
while
retaining
the
characteristics of a probability sample.
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16. SAMPLING PROCESS
Defining the target
population.
Specifying the sampling
frame.
Specifying the sampling
unit.
Selection of the
sampling method.
Determination of
sample size.
Specifying the sampling
plan.
Selecting the sample.
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17. Advantages of sampling

Helps to collect vital information more quickly and it
helps to make estimates of the characteristics of the total
population in a shorter time

Sampling cuts costs. Much of time and money is saved at
each stage of research

Sampling techniques often increases the accuracy of the
data. With small samples it become easier to check the
accuracy of the data.

From the administrative point of view also sampling
become easier – problem of hiring the staff, task of
training and supervising will become easier
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18. Disadvantages of sampling

Sampling is not flexible in a situation where
knowledge about each unit is needed. E.g. estimation
of national income for the current year.

Reliability of information depends upon the
representativeness of the sample of the total
population

Most of the sampling techniques require the service of
a sampling experts or statisticians.

Hospital patients may be different than those in the
community

Volunteers are not typical of non-volunteers.
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19. Standard Error of Mean
Number
Mean DBP
SD ( mmHg)
Printers
72
88
4.5
Farmers
48
79
4.2

SEM = SD√n.

Printers SEM = 4.5 √72 = 0.53mmHg

Farmers SEM = 4.2 √ 48 = 0.61 mmHg
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20. Standard Error of Percentage

Total No. of patients diagnosed with Appendicitis = 120

No. of Males = 73 ( 60.8%)

No. of Females = 47 ( 39.2%)

If P represents one percentage then 100 – P is percentage
for the other so

SE Percentage =
n


P(100-P)
SE Percentage =
60.8 * 39.2/120 = 4.46
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