The document discusses various methods of sampling used in business statistics, including probability and non-probability sampling. Probability sampling methods ensure that every element in the population has a known chance of being selected in the sample. These include random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling, and probability proportional to size sampling. Non-probability sampling methods do not assign a measurable probability to being selected, and include quota sampling, accidental sampling, purposive sampling, voluntary sampling, and snowball sampling. The document also outlines advantages and disadvantages of different sampling techniques.

1. SAMPLING
BUSINESS STATISTICS ASSIGNMENT
INCLUDES VARIOUS METHODS OF SAMPLING, DEMERITS AND
MERITS OF SAMPLING
DEEPAK YADAV,
MBA SEC-A
10/22/2012

2. Sampling
Sampling is a fundamental aspect of statistics, but unlike the
other methods of data collection, sampling involves choosing a
method of sampling which further influences the data that you
will result with. There are two major categories in sampling:
1. Probability and
2. Non-probability sampling.
Probability Sampling
Under probability sampling, for a given population, each
element of that population has a chance of being picked to part
of the sample. In other words, no single element of the
population has a zero chance of being picked
The odd/chances/probability of picking any element is known or
can be calculated. This is possible if we know the total number
in the entire population such that we are then able to determine
that odds of picking any one element.
Probability sampling involves random picking of elements from
a population, and that is the reason as to why no element has a
zero chance of being picked to be part of a sample.

3. Methods of Probability Sampling
There are a number of different methods of probability sampling
including:
Random Sampling
Random sampling is the method that most closely defines
probability sampling. Each element of the sample is picked at
random from the given population such that the probability of
picking that element can be calculated by simply dividing the
frequency of the element by the total number of elements in the
population. In this method, all elements are equally likely to be
picked if they have the same frequency.
Systematic Sampling
Systematic sampling is the method that involves arranging the
population in a given order and then picking the nth element
from the ordered list of all the elements in the population. The
probability of picking any given element can be calculated but is
not likely to be the same for all elements in the population
regardless of whether they have the same frequency.
Stratified Sampling
Stratified sampling involves dividing the population into groups
and then sampling from those different groups depending on a
certain set criteria.

4. For example, dividing the population of a certain class into boys
and girls and then from those two different groups picking those
who fall into the specific category that you intend to study with
your sample.
Cluster Sampling
Cluster sampling involves dividing up the population into
clusters and assigning each element to one and only one cluster,
in other words, an element can't appear in more than one cluster.
Multistage Sampling
Multistage sampling involves use of more than one probability
sampling method and more than one stage of sampling, for
example for using the stratified sampling method in the first
stage and then the random sampling method in the second stage
and so on until you achieve the sample that you want.
Probability Proportional to Size Sampling
Under probability proportional to size sampling, the sample is
chosen as a proportion to the total size of the population. It is a
form of multistage sampling where in stage one you cluster the
entire population and then in stage two you randomly select
elements from the different clusters, but the number of elements
that you select from each cluster is proportional to the size of the
population of that cluster.
Non-Probability Sampling
Unlike probability sampling, under non-probability sampling
certain elements of the population might have a zero chance of

5. being picked. This is because we can't accurately determine the
chances/probability of picking a given element so we do not
know whether the odds of picking that element are zero or
greater than zero. Non-probability sampling may not always be a
consequence of the sampler's ignorance of the total number of
elements in the population but may be a result of the sampler's
bias in the way he chooses the sample by excluding some
elements.
Methods of Non-Probability Sampling
There are a number of different methods of Non-probability
sampling which include:
Quota Sampling
Quota sampling is similar to stratified sampling only that in this
case, after the population is divided into groups, the elements are
then sampled from the group using the sampler's judgement and
as a consequence the method loses any aspect of being random
and can be extremely biased.
Accidental or Convenience Sampling
Accidental sampling is a method of sampling where by the
sampler picks the sample based on the fact that the elements that
he/she picks are conveniently close at the moment. For example,
if you walked down the street and sampled the first ten people
you meet, the fact that they happened to be there is convenient
for you but accidental for them which leads to the name of the
method.

6. Purposive or Judgmental Sampling
Purposive or judge mental sampling is a method of sampling
where by the sampler picks the sample from the entire
population solely based on the his/her judgment. The sampler
controls to a very large extend which elements have a chance of
being selected to be in the sample and which ones don't.
Voluntary Sampling
Voluntary sampling, as the name suggests, involves picking the
sample based on which elements of the population volunteer to
participate in the sample. This is the most common method used
in research polls.
Snowball Sampling
Snowball sampling is a method of sampling that relies on
referrals of previously selected elements to pick other elements
that will participate in the sample.

7. ADVANTAGES AND DISADVANTAGES OF
SAMPLING
Technique Descriptions Advantages Disadvantages
Simple
random
Random sample from
whole population
Highly representative if all
subjects participate; the ideal
Not possible without complete
list of population members;
potentially uneconomical to
achieve; can be disruptive to
isolate members from a group;
time-scale may be too long,
data/sample could change
Stratified
random
Random sample from
identifiable groups
(strata), subgroups, etc.
Can ensure that specific
groups are represented, even
proportionally, in the
sample(s) (e.g., by gender),
by selecting individuals from
strata list
More complex, requires greater
effort than simple random;
strata must be carefully defined
Cluster Random samples of
successive clusters of
subjects (e.g., by
institution) until small
groups are chosen as
units
Possible to select randomly
when no single list of
population members exists,
but local lists do; data
collected on groups may
avoid introduction of
confounding by isolating
members
Clusters in a level must be
equivalent and some natural
ones are not for essential
characteristics (e.g., geographic:
numbers equal, but
unemployment rates differ)
Stage Combination of cluster
(randomly selecting
clusters) and random or
stratified random
sampling of individuals
Can make up probability
sample by random at stages
and within groups; possible
to select random sample
when population lists are
very localized
Complex, combines limitations
of cluster and stratified random
sampling
Purposive Hand-pick subjects on
the basis of specific
characteristics
Ensures balance of group
sizes when multiple groups
are to be selected
Samples are not easily defensible
as being representative of
populations due to potential
subjectivity of researcher
Quota Select individuals as they
come to fill a quota by
Ensures selection of
adequate numbers of
Not possible to prove that the
sample is representative of

8. characteristics
proportional to
populations
subjects with appropriate
characteristics
designated population
Snowball Subjects with desired
traits or characteristics
give names of further
appropriate subjects
Possible to include members
of groups where no lists or
identifiable clusters even
exist (e.g., drug abusers,
criminals)
No way of knowing whether the
sample is representative of the
population
Volunteer,
accidental,
convenience
Either asking for
volunteers, or the
consequence of not all
those selected finally
participating, or a set of
subjects who just happen
to be available
Inexpensive way of ensuring
sufficient numbers of a study
Can be highly unrepresentative
THANK YOU