Types of Sampling: Sampling Methods with Examples
What is sampling?
Sampling definition: Sampling is a technique of selecting individual members or a subset of the
population to make statistical inferences from them and estimate characteristics of the whole
population. Different sampling methods are widely used by researchers in market research so that
they do not need to research the entire population to collect actionable insights. It is also a timeconvenient and a cost-effective method and hence forms the basis of any research design. Sampling
techniques can be used in a research survey software for optimum derivation.
For example, if a drug manufacturer would like to research the adverse side effects of a drug on the
country’s population, it is almost impossible to conduct a research study that involves everyone. In
this case, the researcher decides a sample of people from each demographic and then researches
them, giving him/her indicative feedback on the drug’s behavior.
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Types of sampling: sampling methods
Sampling in market research is of two types – probability sampling and non-probability sampling.
Let’s take a closer look at these two methods of sampling.
1. Probability sampling: Probability sampling is a sampling technique where a researcher sets a
selection of a few criteria and chooses members of a population randomly. All the members have
an equal opportunity to be a part of the sample with this selection parameter.
2. Non-probability sampling: In non-probability sampling, the researcher chooses members for
research at random. This sampling method is not a fixed or predefined selection process. This
makes it difficult for all elements of a population to have equal opportunities to be included in a
sample.
In this blog, we discuss the various probability and non-probability sampling methods that you can
implement in any market research study.
Types of probability sampling with examples:
Probability sampling is a sampling technique in which researchers choose samples from a larger
population using a method based on the theory of probability. This sampling method considers every
member of the population and forms samples based on a fixed process.
For example, in a population of 1000 members, every member will have a 1/1000 chance of being
selected to be a part of a sample. Probability sampling eliminates bias in the population and gives all
members a fair chance to be included in the sample.
There are four types of probability sampling techniques:
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Simple random sampling: One of the best probability sampling techniques that helps in saving
time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining
information where every single member of a population is chosen randomly, merely by chance.
Each individual has the same probability of being chosen to be a part of a sample.
For example, in an organization of 500 employees, if the HR team decides on conducting team
building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case,
each of the 500 employees has an equal opportunity of being selected.
Cluster sampling: Cluster sampling is a method where the researchers divide the entire
population into sections or clusters that represent a population. Clusters are identified and included
in a sample based on demographic parameters like age, sex, location, etc. This makes it very
simple for a survey creator to derive effective inference from the feedback.
For example, if the United States government wishes to evaluate the number of immigrants living in
the Mainland US, they can divide it into clusters based on states such as California, Texas, Florida,
Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as
the results will be organized into states and provide insightful immigration data.
Systematic sampling: Researchers use the systematic sampling method to choose the sample
members of a population at regular intervals. It requires the selection of a starting point for the
sample and sample size that can be repeated at regular intervals. This type of sampling method
has a predefined range, and hence this sampling technique is the least time-consuming.
For example, a researcher intends to collect a systematic sample of 500 people in a population of
5000. He/she numbers each element of the population from 1-5000 and will choose every 10th
individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).
Stratified random sampling: Stratified random sampling is a method in which the researcher
divides the population into smaller groups that don’t overlap but represent the entire population.
While sampling, these groups can be organized and then draw a sample from each group
separately.
For example, a researcher looking to analyze the characteristics of people belonging to different
annual income divisions will create strata (groups) according to the annual family income. Eg – less
than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the
researcher concludes the characteristics of people belonging to different income groups. Marketers
can analyze which income groups to target and which ones to eliminate to create a roadmap that
would bear fruitful results.
Uses of probability sampling
There are multiple uses of probability sampling. They are:
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Reduce Sample Bias: Using the probability sampling method, the bias in the sample derived from
a population is negligible to non-existent. The selection of the sample mainly depicts the
understanding and the inference of the researcher. Probability sampling leads to higher quality data
collection as the sample appropriately represents the population.
Diverse Population: When the population is vast and diverse, it is essential to have adequate
representation so that the data is not skewed towards one demographic. For example, if Square
would like to understand the people that could make their point-of-sale devices, a survey conducted
from a sample of people across the US from different industries and socio-economic backgrounds
helps.
Create an Accurate Sample: Probability sampling helps the researchers plan and create an
accurate sample. This helps to obtain well-defined data.
Types of non-probability sampling with examples
The non-probability method is a sampling method that involves a collection of feedback based on a
researcher or statistician’s sample selection capabilities and not on a fixed selection process. In most
situations, the output of a survey conducted with a non-probable sample leads to skewed results,
which may not represent the desired target population. But, there are situations such as the
preliminary stages of research or cost constraints for conducting research, where non-probability
sampling will be much more useful than the other type.
Four types of non-probability sampling explain the purpose of this sampling method in a better
manner:
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Convenience sampling: This method is dependent on the ease of access to subjects such as
surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience
sampling, because of the researcher’s ease of carrying it out and getting in touch with the subjects.
Researchers have nearly no authority to select the sample elements, and it’s purely done based on
proximity and not representativeness. This non-probability sampling method is used when there are
time and cost limitations in collecting feedback. In situations where there are resource limitations
such as the initial stages of research, convenience sampling is used.
For example, startups and NGOs usually conduct convenience sampling at a mall to distribute
leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance
and giving out pamphlets randomly.
Judgmental or purposive sampling: Judgemental or purposive samples are formed by the
discretion of the researcher. Researchers purely consider the purpose of the study, along with the
understanding of the target audience. For instance, when researchers want to understand the
thought process of people interested in studying for their master’s degree. The selection criteria will
be: “Are you interested in doing your masters in …?” and those who respond with a “No” are
excluded from the sample.
Snowball sampling: Snowball sampling is a sampling method that researchers apply when the
subjects are difficult to trace. For example, it will be extremely challenging to survey shelterless
people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few
categories to interview and derive results. Researchers also implement this sampling method in
situations where the topic is highly sensitive and not openly discussed—for example, surveys to
gather information about HIV Aids. Not many victims will readily respond to the questions. Still,
researchers can contact people they might know or volunteers associated with the cause to get in
touch with the victims and collect information.
Quota sampling: In Quota sampling, the selection of members in this sampling technique
happens based on a pre-set standard. In this case, as a sample is formed based on specific
attributes, the created sample will have the same qualities found in the total population. It is a rapid
method of collecting samples.
Uses of non-probability sampling
Non-probability sampling is used for the following:
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Create a hypothesis: Researchers use the non-probability sampling method to create an
assumption when limited to no prior information is available. This method helps with the immediate
return of data and builds a base for further research.
Exploratory research: Researchers use this sampling technique widely when conducting
qualitative research, pilot studies, or exploratory research.
Budget and time constraints: The non-probability method when there are budget and time
constraints, and some preliminary data must be collected. Since the survey design is not rigid, it is
easier to pick respondents at random and have them take the survey or questionnaire.
How do you decide on the type of sampling to use?
For any research, it is essential to choose a sampling method accurately to meet the goals of your
study. The effectiveness of your sampling relies on various factors. Here are some steps expert
researchers follow to decide the best sampling method.
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Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy.
Identify the effective sampling techniques that might potentially achieve the research goals.
Test each of these methods and examine whether they help in achieving your goal.
Select the method that works best for the research.
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Difference between Probability Sampling and Non-Probability Sampling Methods
We have looked at the different types of sampling methods above and their subtypes. To encapsulate
the whole discussion, though, the significant differences between probability sampling methods and
non-probability sampling methods are as below:
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 or selection
criteria of the sample are undefined.
Results
This type of sampling is entirely unbiased and
hence the results are unbiased too and
conclusive.
This type of sampling is entirely
biased and hence the results are
biased too, rendering the research
speculative.
Hypothesis
In probability sampling, there is an underlying
hypothesis before the study begins and the
objective of this method is to prove the
hypothesis.
In non-probability sampling,
the hypothesis is derived after
conducting the research study.
An introduction to sampling methods
Published on September 19, 2019 by Shona McCombes. Revised on September 8, 2020.
When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group.
Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.
To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is
representative of the group as a whole. There are two types of sampling methods:
•
Probability sampling involves random selection, allowing you to make statistical inferences about the whole
group.
•
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to
easily collect initial data.
You should clearly explain how you selected your sample in the methodology section of your paper or thesis.
Population vs sample
First, you need to understand the difference between a population and a sample, and identify the target population of
your research.
•
The population is the entire group that you want to draw conclusions about.
•
The sample is the specific group of individuals that you will collect data from.
The population can be defined in terms of geographical location, age, income, and many other characteristics.
It can be very broad or quite narrow: maybe you want to make inferences about the whole adult population of your
country; maybe your research focuses on customers of a certain company, patients with a specific health condition, or
students in a single school.
It is important to carefully define your target population according to the purpose and practicalities of your project.
If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access
to a representative sample.
Sampling frame
The 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).
Example
You are doing research on working conditions at Company X. Your population is all 1000 employees of the company.
Your sampling frame is the company’s HR database which lists the names and contact details of every employee.
Sample size
The number of individuals in your sample depends on the size of the population, and on how precisely you want the
results to represent the population as a whole.
You can use a sample size calculator to determine how big your sample should be. In general, the larger the sample size,
the more accurately and confidently you can make inferences about the whole population.
Probability sampling methods
Probability sampling means that every member of the population has a chance of being selected. It is mainly used in
quantitative research. If you want to produce results that are representative of the whole population, you need to use a
probability sampling technique.
There are four main types of probability sample.
1. Simple random sampling
In a simple random sample, every member of the population has an equal chance of being selected. Your sampling
frame should include the whole population.
To conduct this type of sampling, you can use tools like random number generators or other techniques that are based
entirely on chance.
Example
You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in
the company database from 1 to 1000, and use a random number generator to select 100 numbers.
2. Systematic sampling
Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of
the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular
intervals.
Example
All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting
point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you
end up with a sample of 100 people.
If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the
sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority,
there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards
senior employees.
3. Stratified sampling
This sampling method is appropriate when the population has mixed characteristics, and you want to ensure that every
characteristic is proportionally represented in the sample.
You divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range,
income bracket, job role).
From the overall proportions of the population, you calculate how many people should be sampled from each subgroup.
Then you use random or systematic sampling to select a sample from each subgroup.
Example
The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the
gender balance of the company, so you sort the population into two strata based on gender. Then you use random
sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.
4. Cluster sampling
Cluster sampling also 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 method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as
there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really
representative of the whole population.
Example
The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles).
You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices
– these are your clusters.
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Non-probability sampling methods
In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a
chance of being included.
This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias, and you can’t use it to make
valid statistical inferences about the whole population.
Non-probability sampling techniques are often appropriate for exploratory and qualitative research. In these types of
research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small
or under-researched population.
1. 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.
Example
You are researching opinions about student support services in your university, so after each of your classes, you ask
your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only
surveyed students taking the same classes as you at the same level, the sample is not representative of all the students
at your university.
2. Voluntary response sampling
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.
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.
3. Purposive sampling
This type of sampling involves the researcher using their judgement to select a sample that is most useful to the
purposes of the research.
It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific
phenomenon rather than make statistical inferences. An effective purposive sample must have clear criteria and
rationale for inclusion.
Example
You want to know more about the opinions and experiences of disabled students at your university, so you purposefully
select a number of students with different support needs in order to gather a varied range of data on their experiences
with student services.
4. Snowball sampling
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.
Example
You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city,
probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in
contact with other homeless people that she knows in the area.
What is quota sampling?
Quota sampling is defined as a non-probability sampling method in which researchers
create a sample involving individuals that represent a population. Researchers choose
these individuals according to specific traits or qualities. They decide and create quotas so
that the market research samples can be useful in collecting data. These samples can be
generalized to the entire population. The final subset will be decided only according to the
interviewer’s or researcher’s knowledge of the population.
For example, a cigarette company wants to find out what age group prefers what brand of
cigarettes in a particular city. He/she applies quotas on the age groups of 21-30, 31-40,
41-50, and 51+. From this information, the researcher gauges the smoking trend among
the population of the city.
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Types of quota sampling:
Quota sampling can be of two kinds – controlled quota sampling and uncontrolled quota
sampling. Here’s what they mean:
Controlled quota sampling:
Controlled quota sampling imposes restrictions on the researcher’s choice of samples.
Here, the researcher is limited to the selection of samples.
Uncontrolled quota sampling:
Uncontrolled quota sampling does not impose any restrictions on the researcher’s choice
of samples. Here, the researcher chooses sample members at will.
Quota sampling example:
Let’s look at a basic example of quota sampling:
A researcher wants to survey individuals about what smartphone brand they prefer to use.
He/she considers a sample size of 500 respondents. Also, he/she is only interested in
surveying ten states in the US. Here’s how the researcher can divide the population by
quotas:
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Gender: 250 males and 250 females
 Age: 100 respondents each between the ages of 16-20, 21-30, 31-40, 41-50, and 51+
 Employment status: 350 employed and 150 unemployed people.
 (Researchers apply further nested quotas . For eg, out of the 150 unemployed people,
100 must be students.)
 Location: 50 responses per state
Depending on the type of research, the researcher can apply quotas based on the
sampling frame. It is not necessary for the researcher to divide the quotas equally. He/she
divides the quotas as per his/her need (as shown in the example where the researcher
interviews 350 employed and only 150 unemployed individuals). Random sampling can be
conducted to reach out to the respondents.
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How to perform quota sampling:
Probability sampling techniques involve a significant amount of rules that the researcher
needs to follow to form samples. But, since quota sampling is a non-probability sampling
technique, there are no rules for formally creating samples. Usually, there are four steps to
form a quota sample. Here are the steps:
1. Divide the sample population into subgroups: With stratified sampling, the
researcher bifurcates the entire population into mutually exhaustive subgroups, i.e., the
elements of each of the subgroups becomes a part of only one of those subgroups.
Here, the researcher applies random selection.
2. Figure out the weightage of subgroups: The researcher evaluates the proportion in
which the subgroups exist in the population. He/she maintains this proportion in the
sample selected using this type of sampling method.
3. For example, if 58% of the people who are interested in purchasing your Bluetooth
headphones are between the age group of 25-35 years, your subgroups also should
have the same percentages of people belonging to the respective age group.
4. Select an appropriate sample size: In the third step, the researcher should select the
sample size while maintaining the proportion evaluated in the previous step. If the
population size is 500, the researcher can pick a sample of 50 elements.
The sample chosen after following the first three steps should represent the target
population.
5. Conduct surveys according to the quotas defined: Make sure to stick to the
predefined quotas to achieve actual actionable results. Don’t survey quotas that are full
and focus on completing surveys for each quota.
Characteristics of quota sampling:
Here are the top ten characteristics of quota sampling
1. Aims to get the best representation of respondents in the final sample.
2. Quotas replicate the population of interest in a real sense.
3. The estimates produced are more representative.
4. The quality of quota samples vary.
5. Saves research data collection time as the sample represents the population.
6. Saves research costs if the quotas accurately represent the population.
7. It monitors the number of types of individuals who take the survey.
8. The researcher always divides the population into subgroups.
9. The sample represents the entire population.
10.
Researchers use the sampling method to identify the traits of a specific group of
people.
Advantages of quota sampling
Here are the top four advantages of quota sampling
1. Saves time: Because of the involvement of a quota for sample creation, this sampling
process is quick and straightforward.
2. Research convenience: By using quota sampling and appropriate research questions,
interpreting information and responses to the survey is a much convenient process for a
researcher.
3. Accurate representation of the population of interest: Researchers effectively
represent a population using this sampling technique. There is no room for overrepresentation as this sampling technique helps researchers to study the population
using specific quotas.
4. Saves money: The budget required for executing this sampling method is minimalistic.
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Applications of quota sampling:
Below are the instances where quota sampling is applied and used.
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In situations where researchers have specific criteria for conducting research, it allows
the selection of subgroups, due to which it becomes extremely convenient for
researchers to obtain desired results. A trait or characteristic can be the filter for
subgroup formation.
The researcher uses this method when he/she has time constraints. Applying quotas
gives the researcher an idea of the whole population of interest in very little time.
Quotas are applied when the researcher is on a tight budget. Instead of researching a
large population, the researcher saves money by using a few quotas to get the whole
picture of the population.
Some research studies do not require pinpoint accuracy due to the nature of the
research project. It is ideal for applying to quota sampling for these studies.
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1. Random route (Random walk) For each randomly-chosen sampling points (e.g., urban units, small cities, or voting
districts), interviewers are assigned with a starting location and provided with instructions on the random walking
rules – e.g., which direction to start, on which side of the streets to walk and which crossroads to take.
Quota sampling is defined as a non-probability sampling method in which researchers create
a sample involving individuals that represent a population. Researchers choose these
individuals according to specific traits or qualities. They decide and create quotas so that
the market research samples can be useful in collecting data. These samples can be
generalized to the entire population. The final subset will be decided only according to the
interviewer’s or researcher’s knowledge of the population.
Self-selection sampling
Self-selection sampling is a type of non-probability sampling technique. Non-probability sampling focuses
on sampling techniques that are based on the judgement of the researcher [see our article Non-probability
sampling to learn more about non-probability sampling]. This article explains (a) what self-selection
sampling is, (b) how to create a self-selection sample, and (c) the advantages and disadvantages of selfsection sampling.
Multi-stage sampling
Multi-stage sampling (also known as multi-stage cluster sampling) is a more complex form of cluster sampling which
contains two or more stages in sample selection. In simple terms, in multi-stage sampling large clusters of population
are divided into smaller clusters in several stages in order to make primary data collection more manageable. It has
to be acknowledged that multi-stage sampling is not as effective as true random sampling; however, it addresses
certain disadvantages associated with true random sampling such as being overly expensive and time-consuming.
Application of Multi-Stage Sampling: an Example
Contrary to its name, multi-stage sampling can be easy to apply in business studies. Application of this sampling
method can be divided into four stages:
1. Choosing sampling frame, numbering each group with a unique number and selecting a small sample of
relevant discrete groups.
2. Choosing a sampling frame of relevant discrete sub-groups. This should be done from relevant discrete groups
selected in the previous stage.
3. Repeat the second stage above, if necessary.
4. Choosing the members of the sample group from the sub-groups using some variation of probability sampling.
Let’s illustrate the application of the stages above using a specific example.
Your research objective is to evaluate online spending patterns of households in the US through online questionnaires.
You can form your sample group comprising 120 households in the following manner:
1. Choose 6 states in the USA using simple random sampling (or any other probability sampling).
2. Choose 4 districts within each state using systematic sampling method (or any other probability sampling).
3. Choose 5 households from each district using simple random or systematic sampling methods. This will result
in 120 households to be included in your sample group.
Advantages of Multi-Stage Sampling
1. Effective in primary data collection from geographically dispersed. population when face-to-face contact in
required (e.g. semi-structured in-depth interviews)
2. Cost-effectiveness and time-effectiveness.
3. High level of flexibility.
Disadvantages of Multi-Stage Sampling
1. High level of subjectivity.
2. Research findings can never be 100% representative of population.
3. The presence of group-level information is required.
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