COURSE OUTLINE NO.3
MGS 083 : QUANTITATIVE APPROACH TO MANAGEMENT
SAMPLING
ANALYSIS
PREPARED BY :
AR. DOROTHY CASTRO
ENGR. JOHN ANDREW DE VERA
ENGR. EDWARD DAVE MASAOY
ENGR. FHRANCIS ABALOS
PRESENTORS :
AR. DOROTHY P.
CASTRO
ENGR. JOHN
ANDREWS DE VERA
ENGR. EDWARD
DAVE MASAOY
ENGR. FHRANCIS
ABALOS
AGENDA AND TOPICS
AR.DOROTHY CASTRO
01
Introductions • Process • Sampling Frame and Size
SAMPLING METHODS
ENGR. JOHN ANDREWS DE VERA
ENGR. JOHN ANDREWS DE VERA
AR.DOROTHY CASTRO
OVERVIEW OF SAMPLING
Types of Sampling Methods
02
PROBABILITY SAMPLING
Types and Examples
NON- PROBABILITY SAMPLING
ENGR. EDWARD DAVE MASAOY
ENGR. FHRANCIS ABALOS
Types and Examples
ENGR. EDWARD DAVE MASAOY
03
IMPORTANCE OF SAMPLING
ENGR.FHRANCIS ABALOS
04
KEY TAKEAWAYS AND REFERENCE
01
OVERVIEW OF
SAMPLING
WHAT IS SAMPLING ?
SAMPLING
is a statistical analysis process that
involves analyzing a small portion of a
dataset to make accurate conclusions
about the whole, saving time and effort.
How Sampling
works ?
Steps in Conducting a Sampling Process
Define the population
Choose a sampling method
Determine the sample size
Collect data from the sample
Analyze and interpret the Data
Difference between Population
vs Sample :
•
Population - is the entire
group that you want to draw
conclusions about.
•
Sample - is the specific
group of individuals that you
will collect data from.
SAMPLING FRAME
•
is the actual list of individuals that
the sample will be drawn from.
Ideally, it should include the entire
target population (and nobody
who is not part of that population).
SAMPLE SIZE
The number of individuals you should
include in your sample depends on various
factors, including the size and variability of
the population and your research design.
There are different sample size calculators
and formulas depending on what you want
to achieve with statistical analysis.
02
SAMPLING METHODS
WHAT IS SAMPLE METHOD ?
The method of collecting data from
a population, regarding a sample on
a group of items and examining it to
draw out some conclusion.
PRIMARY TYPES OF SAMPLING METHODS
PROBABILITY SAMPLING
involves random selection, allowing you to
make strong statistical inferences about
the whole group.
NON- PROBABILITY SAMPLING
involves non-random selection based on
convenience or other criteria, allowing you
to easily collect data.
PROBABILITY SAMPLING
METHODS
PRIMARY TYPES OF
SAMPLING METHODS
PROBABILITY SAMPLING
means that every member of the
population has a chance of being
selected. It is mainly used
in quantitative research.
TYPES OF PROBABILITY SAMPLING
SIMPLE RANDOM SAMPLE
SYSTEMATIC SAMPLE
STRATIFIED SAMPLE
CLUSTER SAMPLE
TYPES OF PROBABILITY SAMPLING
A. SIMPLE RANDOM SAMPLE
•
In a simple random sample, every member
of the population has an equal chance of
being selected.
•
To conduct this type of sampling, you can
use tools like random number generators or
other techniques that are based entirely on
chance
•
There are two ways of collecting data
through the random sampling method.
These are the Lottery Method and Tables
of Random Numbers..
SIMPLE RANDOM SAMPLING
Example:
In this example, members of a population are numbered and put into a hat.
Tommy randomly picks two numbers from the hat. He then returns them and
chooses again. Because of this, the population has an equal chance of being
selected.
SIMPLE RANDOM SAMPLING EXAMPLES :
Engineering
• A manufacturing company wants to assess the quality of a batch of 1000 screws. They could use simple random
sampling to select 100 screws at random for inspection, ensuring each screw has an equal chance of being
Quality Control:
• To ensure the quality of products coming off an assembly line, an engineer might randomly
select items to test for defects.
Material Testing:
• When testing the strength or durability of materials, an engineer could randomly select samples from
a batch to ensure the results are representative of the entire batch.
Surveying a Population:
• If an engineer wants to gauge public opinion on a new product, they might randomly select
individuals from a target demographic to participate in a survey.
SIMPLE RANDOM SAMPLING
MERITS
•
•
•
•
Random Sampling method is economical as
the items are selected randomly, which can
be done by fewer people and with fewer
resources.
Random Sampling method is impartial and
free from personal biases, as it randomly
selects the numbers, and each of the items
has an equal probability of getting selected.
This method fairly represents the universe
through samples.
It is a straightforward and simple method of
collecting data.
DEMERITS
•
•
Despite its various advantages, the
random sampling method does not give
proper weightage to some important
items of the universe.
Also, there is no guarantee that different
items of the universe are proportionately
represented.
TYPES OF PROBABILITY SAMPLING
•
Systematic sampling is similar to
simple random sampling, but it is
usually slightly easier to conduct.
•
Different units of the population are
systematically arranged in numerical,
alphabetical, and geographical order.
•
To form a sample, every nth term or
item of the numbered items is
selected. This method is a short-cut
method of collecting data through the
Random Sampling method.
B. SYSTEMATIC SAMPLE
SYSTEMATIC SAMPLING
Example:
This example shows a neighborhood that will be systematically sampled. First we
choose a starting point. Then we systematically choose every third house to
survey. This is just one example of systematic sampling.
SYSTEMATIC SAMPLING EXAMPLES :
Engineering Example:
A construction company needs to test the strength of concrete delivered to a site. They could use systematic sampling to
test every 10th truckload of concrete delivered, ensuring a consistent interval for testing.
Quality Control in Production Line
• Imagine an engineer wants to check the quality of 1000 manufactured parts on a production line, but
testing every single part is impractical.
• Systematic Sampling: The engineer could systematically select every 10th part for testing, starting at a
random point (e.g., the 3rd part). This ensures a representative sample of the entire production run.
Investigation:
• if 10 out of 200 people are to be selected for investigation, then these are first arranged in a
systematic order. After that one of the first 10 people would be randomly selected. In the
same way, every 10th person from the selected item will be taken under the sample.
SYSTEMATIC SAMPLING
MERITS
•
Systematic Sampling Method is a
simple method of collecting data as
the investigator can easily determine
the sample.
•
As the items are arranged in a
systematic order, the chances of
personal biases are less.
DEMERITS
•
As the first item of the given population
is selected randomly, and then further
items are selected on the basis of the
first item, every item of the population
does not get an equal chance of getting
selected.
•
In case the population has homogeneous
items, the method of Systematic
Sampling does not serve any specific
purpose.
TYPES OF PROBABILITY SAMPLING
C. STRATIFIED SAMPLE
•
involves dividing the population into
subpopulations that may differ in important
ways. It allows you draw more precise
conclusions by ensuring that every subgroup
is properly represented in the sample.
•
To use this sampling method, you divide the
population into subgroups (called strata)
based on the relevant characteristic (e.g.,
gender identity, age range, income bracket,
job role).
STRATIFIED SAMPLING
Example:
This example shows how the population is grouped together by some sort of
characteristic. Then Joe chooses one from each group to make up his team. This
is just one example of a stratified sampling.
STRATIFIED SAMPLING EXAMPLES :
Engineering Example:
• An electrical engineering firm wants to assess the performance of different types of light bulbs. They could stratify the
bulbs based on wattage and then sample bulbs from each wattage group to ensure that each type of bulb is
adequately represented in the sample.
Structural Design Evaluation:
• If a company wants to assess the failure rate of buildings they designed, they could stratify the population of
buildings by factors like building material (steel, concrete, wood), structural type (frame, shear wall), and
functionality (residential, commercial, industrial) and then take random samples from each stratum.
Material Testing:
• When testing the strength of different types of concrete mixes, you could stratify the population of
concrete samples based on the type of aggregate used (e.g., crushed stone, gravel, recycled
concrete) and then randomly sample from each stratum to ensure each type of aggregate is
represented proportionally in your testing.
STRATIFIED SAMPLING
MERITS
•
•
•
As different groups of a population with
different characteristics are selected in this
method of sampling, it covers a large
portion of the characteristics of the
population.
Selection of the diverse characteristics of
the population makes the comparative
analysis of the data possible.
The Stratified Method of Sampling offers
meaningful and reliable results to the
investigator.
DEMERITS
•
The Stratified Sampling Method has a limited
scope because it is suitable only when the
investigator has complete knowledge of the
diverse characteristics of the entire population.
•
As the population is divided into different strata
by the investigator himself, there are chances of
biasness in this step.
•
In the case of a small population, it may be
difficult for the investigator to divide the
population into small strata.
TYPES OF PROBABILITY SAMPLING
•
•
Involves dividing the population into
subgroups, but each subgroup should have
similar characteristics to the whole sample.
Instead of sampling individuals from each
subgroup, you randomly select entire
subgroups
If it is practically possible, you might include
every individual from each sampled cluster.
If the clusters themselves are large, you can
also sample individuals from within each
cluster using one of the techniques above.
This is called multistage sampling.
D. CLUSTERED SAMPLING
CLUSTERED SAMPLING
Example:
This example shows clusters of individuals separated by their street/avenue. Joe
then chooses one street/avenue to do his survey. This is just one example of a
cluster sampling.
CLUSTERED SAMPLING EXAMPLES :
Engineering Example:
• A civil engineering firm wants to assess the quality of roads in a city. They could divide the city into districts
(clusters) and then randomly select a few districts to inspect, rather than inspecting every road in the entire
city.
Quality Control in Manufacturing:
• Scenario: A large manufacturing plant produces a product on multiple assembly lines. To
assess the overall quality, instead of testing every product, the plant can randomly select a
few assembly lines (clusters) and test all products from those lines.
• Clusters: Assembly lines.
• Sampling Method: Randomly select a number of assembly lines, then test every product from
those selected lines.
CLUSTERED SAMPLING EXAMPLES :
Civil Engineering Infrastructure Assessment:
• Scenario: A city wants to assess the condition of its bridges. Instead of inspecting every bridge, they
can randomly select a few bridge clusters (e.g., bridges of the same type or in the same geographical
area) and inspect all bridges within those clusters.
• Clusters: Bridges of the same type or in the same geographical area.
• Sampling Method: Randomly select clusters, then inspect all bridges within those clusters.
Product Testing:
• Scenario: A company wants to test the durability of a new type of plastic. Instead of testing
every piece of plastic, they can randomly select a few batches (clusters) and test all pieces
within those batches.
• Clusters: Batches of plastic.
• Sampling Method: Randomly select batches, then test all pieces within those batch
CLUSTERED SAMPLING EXAMPLES :
MERITS
•
Cluster sampling is often more economical and
quicker than other methods, especially for large,
geographically dispersed populations, as it reduces
travel and administrative costs by focusing on
specific areas or group
•
It makes large-scale studies more manageable,
particularly when a complete list of all population
members is unavailable.
•
The process is easier to execute than some other
sampling methods, as it doesn't require a
comprehensive list of all population members.
DEMERITS
•
Clustering can lead to higher variability within a
sample compared to simple random sampling,
resulting in less precise estimates
•
If clusters are not chosen carefully or are not
representative of the overall population, there is
a risk of bias, potentially skewing the study
results.
•
Cluster sampling provides less statistical
certainty than other methods, such as simple
random sampling, because it is difficult to
ensure that clusters properly represent the
population as a whole
When do we Use
Probability
Sampling?
Non-Probability Sampling
Methods
PRIMARY TYPES OF
SAMPLING METHODS
NON- PROBABILITY
SAMPLING
individuals are selected based
on non-random criteria, and
not every individual has a
chance of being included.
TYPE OF NON-PROBABILITY SAMPLING METHODS
CONVENIENCE SAMPLE
PURPOSIVE SAMPLE
SNOW BALL SAMPLE
QUOTA SAMPLE
TYPE OF NON-PROBABILITY SAMPLING METHODS
A. CONVENIENCE SAMPLING
•
A convenience sample simply
includes the individuals who happen
to be most accessible to the
researcher.
•
This is an easy and inexpensive way
to gather initial data, but there is no
way to tell if the sample is
representative of the population, so it
can’t produce generalizable results.
CONVENIENCE SAMPLING
Example:
This example shows Joe wanting to survey his friends but is inconvenienced by
the distance he would have to travel to get a good sample. He instead samples
the friends closest to him. This is just one example of convenience sampling.
CONVENIENCE SAMPLING EXAMPLES :
Engineering Example:
• A researcher studying the noise levels in a factory might choose to measure noise levels in the areas where they are
working, rather than going to different parts of the factory.
User Feedback on a New Design::
• An engineer could quickly gather feedback on a new product design by asking colleagues or friends who are
readily available to provide their opinions.
Gathering Information on a New Material:
• Engineers can use convenience sampling to gather information on the properties of a new material
by testing the material with readily available equipment or by asking colleagues who are experts in
materials science.
CONVENIENCE SAMPLING
MERITS
•
The Convenience Sampling
Method is the least expensive
method of collecting data.
•
It is also the simplest method of
collecting data from the
population.
DEMERITS
•
This method is highly unreliable,
as the investigator selects the
items that suit him, and it is not
possible that every investigator
has reliable thinking or purpose of
investigation. Besides, different
investigators have different
perspectives.
TYPE OF NON-PROBABILITY SAMPLING METHODS
•
Similar to a convenience sample, a
voluntary response sample is mainly
based on ease of access. Instead of
the researcher choosing participants
and directly contacting them, people
volunteer themselves (e.g. by
responding to a public online survey).
•
Voluntary response samples are
always at least somewhat biased, as
some people will inherently be more
likely to volunteer than others, leading
to self-selection bias..
B.VOLUNTARY SAMPLING
VOLUNTARY SAMPLING
Example:
•
You send out the survey to all students at your university and a lot of students decide to complete
it. This can certainly give you some insight into the topic, but the people who responded are more
likely to be those who have strong opinions about the student support services, so you can’t be
sure that their opinions are representative of all students.
Example:
• Suppose you are doing an environmental survey.
• Imagine researchers want to know what people think about protecting the environment. They create an online
survey and share the link on social media.Anyone who sees the link can choose to fill out the survey. Only those
who care about environmental issues will likely respond. Therefore, the results will reflect the opinions of engaged
individuals, but not necessarily everyone in the population
TYPE OF NON-PROBABILITY SAMPLING METHODS
C. PURPOSIVE SAMPLING
•
This type of sampling, also known as
judgement sampling, involves the
researcher using their expertise to
select a sample that is most useful to
the purposes of the research.
•
The method in which the investigator
himself selects the sample of his
choice, which in his opinion is best
to represent the universe
PURPOSIVE SAMPLING EXAMPLES
Investigating a new material's performance:
• If engineers are studying a new composite material's strength under various stress conditions, they might purposively
select samples with different fiber orientations or resin types to explore the material's behavior across a range of
conditions.
Assessing the effectiveness of a new design
• To evaluate the performance of a new bridge design, engineers might purposively select bridges with similar
spans, traffic loads, and environmental conditions to compare their performance and identify any design
flaws.
Understanding user experience with a new product:
• When designing a new product, engineers might purposively select users with specific demographics, technical
expertise, or usage patterns to gain insights into how different user groups interact with the product.
PURPOSIVE SAMPLING EXAMPLES
Assessing Building Materials:
• Instead of testing every type of building material, you might purposively select materials known for their durability,
sustainability, or resistance to specific conditions (e.g., corrosion, fire, or earthquakes) to evaluate their long-term
performance.
Studying Traffic Flow:
• You could purposively select intersections or road sections known for high traffic congestion or specific
traffic patterns (e.g., rush hour bottlenecks, peak hours, or commuter routes) to study traffic flow and
identify potential solutions.
Investigating Construction Site Safety:
• You could purposively select construction sites known for specific safety hazards (e.g., high-rise construction,
tunneling projects, or those involving hazardous materials) to study safety protocols and identify areas for
improvement.
PURPOSIVE SAMPLING
MERITS
1.
2.
3.
The Purposive or Deliberate
Sampling Method is flexible, as it
allows an investigator to include
items with special significance in the
sample.
The investigator can easily tune the
selection of items based on the
purpose of the investigation, making
it easy for him to perform the
analysis.
It is a very simple technique of
collecting data, as the investigator
can select the significant items in
the sample by his choice.
DEMERITS
1.
As the investigator can
select an item in the sample
for the investigation, the
probability of personal
biases increases.
2.
An increase in the
probability of personal
biases makes the method
less reliable for collecting
data, and the results
become doubtful.
TYPE OF NON-PROBABILITY SAMPLING METHODS
•
If the population is hard to access,
snowball sampling can be used to recruit
participants via other participants. The
number of people you have access to
“snowballs” as you get in contact with
more people.
•
The downside here is also
representativeness, as you have no way
of knowing how representative your
sample is due to the reliance on
participants recruiting others. This can
lead to sampling bias.
D. SNOWBALL SAMPLING
SNOWBALL SAMPLING EXAMPLES
Studying a specific software development team:
• If you want to understand the workflow and challenges of a team using a particular coding framework, you
could start by interviewing one member and then ask them to refer other team members for interviews.
Investigating a rare equipment failure:
• When investigating a specific type of equipment failure that's not common, you could start by identifying an engineer
who has experience with that equipment and then ask them to recommend other engineers who might have
encountered the same issue.
Understanding user experience with a new product:
• To gather feedback on a new product, you could start by interviewing a few early adopters and then ask
them to refer other users who have also used the product
SNOWBALL SAMPLING EXAMPLES
Evaluating a specific engineering design:
• If you are trying to evaluate a specific design for a product, you could start with a few
engineers who have experience with that type of design and then ask them to
recommend other engineers who have also used the same design.
How it works:
• Identify an initial participant: Start with a person who fits the criteria of your study.
• Conduct an initial interview or survey: Gather information from the first participant.
• Ask for referrals: Ask the first participant to recommend other people who fit the criteria
of your study.
• Recruit the referrals: Contact the recommended participants and conduct interviews or
surveys.
• Continue the process: Repeat steps 3 and 4 until you have a sufficient sample size.
TYPE OF NON-PROBABILITY SAMPLING METHODS
E. QUOTA SAMPLING
•
Relies on the non-random selection of a
predetermined number or proportion of units.
This is called a quota
•
You first divide the population into mutually
exclusive subgroups (called strata) and then
recruit sample units until you reach your
quota.
•
These units share specific characteristics,
determined by you prior to forming your
strata. The aim of quota sampling is to control
what or who makes up your sample.
QUOTA SAMPLING EXAMPLES
Public Opinion on Infrastructure Projects:
• Scenario: You're planning a new highway project and want to gauge public opinion on
potential routes and environmental impacts.
• Quota Sampling Approach:
• Identify key demographics: Determine the relevant subgroups (e.g., residents living
near the proposed route, commuters, business owners).
• Set quotas: Establish the number of participants from each subgroup to ensure
representation. For example, if 60% of the population affected by the project are
commuters, ensure your sample includes 60% commuters.
• Conduct surveys: Approach individuals in the target areas and conduct surveys or
interviews, ensuring you meet the quotas for each subgroup.
QUOTA SAMPLING EXAMPLES
Material Testing and Quality Control:
• Scenario: You need to assess the quality of concrete used in a bridge project.
• Quota Sampling Approach:
• Identify key factors: Determine the relevant factors (e.g., concrete mix, age of concrete, location of samples).
• Set quotas: Establish the number of samples to be tested from each mix, age, and location.
• Conduct tests: Collect concrete samples and perform tests, ensuring that the quotas are met for each factor.
Traffic Flow Studies:
• Scenario: You're studying traffic flow patterns on a major road to plan for future improvements.
• Quota Sampling Approach:
• Identify key factors:Determine the relevant factors (e.g., time of day, type of vehicle,
origin/destination)
• Set quotas: Establish the number of observations to be made for each factor
• Conduct tests: Observe traffic flow patterns, ensuring that the quotas are met for each factor.
QUOTA SAMPLING
MERITS
DEMERITS
•
•The chances of
personal biases while selecting
the items in a sample are high.
The Quota Sampling Method of
collecting data is affordable.
•Personal biases during the
selection of items in a sample
make the reliability of the results
through investigation
questionable.
Here’s how you differentiate probability sampling from non-probability
sampling :
PROBABILITY SAMPLING
NON-PROBABILITY SAMPLING
The samples are randomly selected.
Samples are selected on the basis of the researcher’s
subjective judgment.
Everyone in the population has an equal chance of
getting selected.
Not everyone has an equal chance to participate.
Researchers use this technique when they want to
keep a tab on sampling bias.
Sampling bias is not a concern for the researcher.
Useful in an environment having a diverse population.
Useful in an environment that shares similar traits.
Used when the researcher wants to create accurate
samples.
This method does not help in representing the
population accurately.
Finding the correct audience is complex.
Finding an audience is very simple.
03
SUMMARY AND
IMPORTANCE OF
SAMPLING
PROBABILITY SAMPLING METHODS
NON-PROBABILITY SAMPLING METHODS
Definition
Probability Sampling is a sampling technique in which samples from
a larger population are chosen using a method based on the theory
of probability.
Non-probability sampling is a sampling technique in which the
researcher selects samples based on the researcher’s subjective
judgment rather than random selection.
Alternatively Known
as
Random sampling method.
Non-random sampling method
Population selection
The population is selected randomly.
The population is selected arbitrarily.
Nature
The research is conclusive.
The research is exploratory.
Sample
Since there is a method for deciding the sample, the population
demographics are conclusively represented.
Since the sampling method is arbitrary, the population
demographics representation is almost always skewed.
Time Taken
Takes longer to conduct since the research design defines the
selection parameters before the market research study begins.
This type of sampling method is quick since neither the sample nor
the selection criteria of the sample are undefined.
Results
This type of sampling is entirely unbiased; hence, the results are also This type of sampling is entirely biased, and hence the results are
conclusive.
biased, too, rendering the research speculative.
Hypothesis
In probability sampling, there is an underlying hypothesis before the
study begins, and this method aims to prove the hypothesis.
In non-probability sampling, the hypothesis is derived after
conducting the research study.
Importance of Sampling in Business
and Finance
As discussed, sampling has many uses. In business in
finance, it is applied in varied ways:
•
Market research: By analyzing a sample of
their target audiences, companies can determine
product fit, gauge the interest in new items, and
refine marketing strategies.
•
Financial auditing: Auditors perform a detailed
analysis of company financials and transactions. They
can choose transaction samples to identify errors and
fraud without having to check every single company
transaction. A sample would still allow auditors to
identify inconsistencies or patterns of inaccurate
reporting.
•
Quality control in manufacturing: To ensure
product quality, manufacturers use sampling without
having to inspect every item produced. If defects are
found in the sample, fixes can be made before the
entire batch is sent out. This helps ensure customer
satisfaction and avoid costly recalls.
USES OF SAMPLING
Sampling is widely used across many industries. Businesses and organizations rely on it to
make critical decisions, and it is particularly common in economic research. For example,
government agencies, such as the Bureau of Labor Statistics (BLS), use sampling to assess
employment trends.
Rather than sampling every single business and household in the U.S., the BLS relies on samples.
The Current Employment Statistics program samples approximately 119,000 businesses and
government agencies, covering about 629,000 work sites. This allows policymakers and economists
to gauge job growth, wage trends, and industry shifts without input from every employer in the
country.
The Current Population Survey samples 60,000 households to track changes in the labor market. It
provides insights into unemployment, workforce participation, and employment across various
demographics. These statistics influence government policy and business hiring decisions.
Uses of Sampling
In addition to economics, sampling is used in many other ways. Companies regularly
conduct product testing on a sample of consumers before rolling out a new item to the
larger public. This is done to gauge interest, issues, and the likely success of the
product.
Rather than sifting through millions of records, sampling is used by financial institutions
to audit transactions to detect fraud. Retailers use sampling to analyze purchasing
patterns rather than tracking every purchase. This can help with estimating future
demand and setting prices.
04
KEY TAKE AWAYS
Key Takeaways
1)
2)
3)
Businesses and governments use sampling for market
research, financial auditing, and employment statistics.
Financial auditing: Many types of sampling methods exist,
including random, stratified, cluster, systematic, and
convenience, all of which are suited to specific situations.
Sampling helps companies make better decisions, from
predicting customer behavior to identifying fraud.
REFERENCES
●
https://towardsdatascience.com/an-introduction-to-probability-sampling-methods7a936e486b5/?source=userActivityShare-4dff0f45cb241638211885&_branch_match_id=1220941796812139940&_branch_referrer=H4sIAAAAAAAAA8soKSkottLXz8nM
y9bLTU3JLM3VS87P1c%2FOTc31dwwpcMxJsq8rSk1LLSrKzEuPTyrKLy9OLbJ1zijKz00FAAzJucw9AAAA
●
https://www.questionpro.com/blog/types-of-sampling-methods/
●
https://www.geeksforgeeks.org/methods-of-sampling/?ref=header_outind
●
https://www.investopedia.com/terms/s/sampling.asp#:~:text=This%20information%20can%20help%20XYZ,with%2
0business%20decisions%20and%20policymaking.
●
https://www.scribbr.com/methodology/sampling-methods/
Thanks!
Slidesgo
CREDITS: This presentation template was created by Slidesgo, and includes
icons by Flaticon, and infographics & images by Freepik
Flaticon
Please keep this slide for attribution
Freepik