This document discusses sampling techniques used in data analysis. It defines sampling as selecting a subset of data to represent a larger population. Two common sampling methods are described: Simple random sampling involves randomly selecting items from the entire population so that each item has an equal chance of selection. Stratified random sampling first divides the population into subgroups, then randomly samples from each subgroup in proportion to its size. This ensures subgroups are represented in the sample. Advantages and disadvantages of each method are provided. Simple random sampling is economical but risks not being representative. Stratified random sampling has less chance of bias and higher accuracy but requires defining subgroups first. Sampling allows analyzing large populations efficiently while capturing overall characteristics.

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2. SAMPLING

3. What is Sampling
A technique of
selecting a
representative part
of a population for
the purpose of
determining
characteristics of the
whole population to
save cost , time and
efforts
Thus, sample data is
the subset of
population data
used to represent
the entire group as a
whole

4. Popular sampling methods
Simple Random
Sampling:
Here, the selection is purely based on a chance and every item has an equal
chance of getting selected
Lottery system is an example of simple random sampling
Stratified Random
Sampling:
Here, the population data is divided into subgroups known as strata
The members in each of the subgroup formed have similar attributes and
characteristics in terms of demographics, income, location etc.
A random sample from each of these subgroups is taken in proportion to the
subgroup size relative to the population size
These subsets of subgroups are then added to from a final stratified random
sample
Higher statistical precision is achieved through this method due to low
variability within each subgroup, also less sample size is required for this
method of sampling when compared to simple random sampling

5. Example : Simple Random SamplingForinstance
if we want to come
up with average
value of all cars in
united states, it is
impractical to
assess the each car
value in united
states, adding
these numbers and
dividing by total
number of cars
Instead
we can randomly
select some of the
cars, say 200 and
get value of each of
these 200 cars and
find average of
these 200 numbers
These
200 numbers
containing
randomly selected
200 cars’ values is
called a sample
data of entire
United states’ cars’
values (population
data)

6. Example : Simple Random SamplingTakinganotherexample
The total
workforce in
organizations is
300 and to
conduct a survey,
a sample group of
30 employees is
selected to do the
survey
Inthiscase
The population is
the total number
of employees in
the company and
the sample group
of 30 employees
is the sample
Eachmember
Of the workforce
has an equal
opportunity of
being chosen
because all the
employees which
were chosen to be
part of the survey
were selected
randomly.
But,
There is always a
possibility that
the sample does
not represent the
population as a
whole

7. Example : Stratified Random Sampling
It is used when the researcher
wants to examine subgroups
within a population
For example, one might divide
a sample of adults into
subgroups by age, like 18-29,
30-39, 40-49, 50-59, and 60
and above
To stratify this sample, the
researcher would then
randomly select
proportional amounts of
people from each age
group
This is an effective sampling
technique for studying how a
trend or issue might differ
across subgroups
Some of the most common
strata used in stratified
random sampling include
age, gender, religion, race,
educational attainment,
socioeconomic status, and
nationality
Thus, with stratified sampling,
the researcher is guaranteed
that the subjects from each
subgroup are included in the
final sample, whereas simple
random sampling does not
ensure that subgroups are
represented equally or
proportionately within the
sample

8. Pros and Cons : Simple Random Sampling
Economical in
nature
Less time
consuming
Chances of
bias
Difficulty of
getting the
representative
sample

9. Pros and Cons : Stratified Random Sampling
Economical in nature
Less time consuming
Very less chances of bias
compared to Simple
random sampling
Higher accuracy than
Simple random sampling
Need to define the
categorical variable by
which sub groups should
be created : For instance
Age group , Gender,
Occupation , Income
group, Education, Religion,
Region etc.

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June 2018