How often do researchers look for the right survey respondents, either for a market research study or an existing survey in the field? The sample or the respondents of this research may be selected from a set of customers or users that are known or unknown.

You may often know your typical respondent profile but don’t have access to the respondents to complete your research study. At such times, researchers and research teams reach out to specialized organizations to access their panel of respondents or buy respondents from them to complete research studies and surveys.

These could be general population respondents that match demographic criteria or respondents based on specific criteria. Such respondents are imperative to the success of research studies.

This article discusses in detail the different types of samples, sampling methods, and examples of each. It also mentions the steps to calculate the size, the details of an online sample, and the advantages of using them.

**Content Index**

- What is a sample?
- Types of samples: Sample selection methodologies with examples
- Probability sampling methodologies with examples
- Non-probability sampling methodologies with examples

- How to determine a sample size
- Calculating sample size
- Sampling advantages

**What is a Sample?**

A sample is a smaller set of data that a researcher chooses or selects from a larger population using a pre-defined selection method. These elements are known as sample points, sampling units, or observations.

Creating a sample is an efficient method of conducting research. Researching the whole population is often impossible, costly, and time-consuming. Hence, examining the sample provides insights the researcher can apply to the entire population.

For example, if a cell phone manufacturer wants to conduct a feature research study among students in US Universities. An in-depth research study must be conducted if the researcher is looking for features that the students use, features they would like to see, and the price they are willing to pay.

This step is imperative to understand the features that need development, the features that require an upgrade, the device’s pricing, and the go-to-market strategy.

In 2016/17 alone, there were 24.7 million students enrolled in universities across the US. It is impossible to research all these students; the time spent would make the new device redundant, and the money spent on development would render the study useless.

Creating a sample of universities by geographical location and further creating a sample of these students from these universities provides a large enough number of students for research.

Typically, the population for market research is enormous. Making an enumeration of the whole population is practically impossible. The sample usually represents a manageable size of this population. Researchers then collect data from these samples through surveys, polls, and questionnaires and extrapolate this data analysis to the broader community.

**Types of Samples: Selection methodologies with examples**

The process of deriving a sample is called a sampling method. Sampling forms an integral part of the research design as this method derives the quantitative and qualitative data that can be collected as part of a research study. Sampling methods are characterized into two distinct approaches: probability sampling and non-probability sampling.

**Probability sampling methodologies with examples**

Probability sampling is a method of deriving a sample where the objects are selected from a population-based on probability theory. This method includes everyone in the population, and everyone has an equal chance of being selected. Hence, there is no bias whatsoever in this type of sample.

Each person in the population can subsequently be a part of the research. The selection criteria are decided at the outset of the market research study and form an important component of research.

Probability sampling can be further classified into four distinct types of samples. They are:

**Simple random sampling:**The most straightforward way of selecting a sample is simple random sampling. In this method, each member has an equal chance of participating in the study. The objects in this sample population are chosen randomly, and each member has the same probability of being selected. For example, if a university dean would like to collect feedback from students about their perception of the teachers and level of education, all 1000 students in the University could be a part of this sample. Any 100 students can be selected randomly to be a part of this sample.**Cluster sampling:**Cluster sampling is a type of sampling method where the respondent population is divided into equal clusters. Clusters are identified and included in a sample based on defining demographic parameters such as age, location, sex, etc. This makes it extremely easy for a survey creator to derive practical inferences from the feedback. For example, if the FDA wants to collect data about adverse side effects from drugs, they can divide the mainland US into distinctive cluster analysis, like states. Research studies are then administered to respondents in these clusters. This type of generating a sample makes the data collection in-depth and provides easy-to-consume and act-upon, insights.**Systematic sampling:**Systematic sampling is a sampling method where the researcher chooses respondents at equal intervals from a population. The approach to selecting the sample is to pick a starting point and then pick respondents at a pre-defined sample interval. For example, while selecting 1,000 volunteers for the Olympics from an application list of 10,000 people, each applicant is given a count of 1 to 10,000. Then starting from 1 and selecting each respondent with an interval of 10, a sample of 1,000 volunteers can be obtained.**Stratified random sampling:**Stratified random sampling is a method of dividing the respondent population into distinctive but pre-defined parameters in the research design phase. In this method, the respondents don’t overlap but collectively represent the whole population. For example, a researcher looking to analyze people from different socioeconomic backgrounds can distinguish respondents by their annual salaries. This forms smaller groups of people or samples, and then some objects from these samples can be used for the research study.

**Non-probability sampling methodologies with examples**

The non-probability sampling method uses the researcher’s discretion to select a sample. This type of sample is derived mostly from the researcher’s or statistician’s ability to get to this sample.

This type of sampling is used for preliminary research where the primary objective is to derive a hypothesis about the topic in research. Here each member does not have an equal chance of being a part of the sample population, and those parameters are known only post-selection to the sample.

We can classify non-probability sampling into four distinct types of samples. They are:

**Convenience sampling:**Convenience sampling, in easy terms, stands for the convenience of a researcher accessing a respondent. There is no scientific method for deriving this sample. Researchers have nearly no authority over selecting the sample elements, and it’s purely done based on proximity and not representativeness.

This non-probability sampling method is used when there is time and costs limitations in collecting feedback. For example, researchers that are conducting a mall-intercept survey to understand the probability of using a fragrance from a perfume manufacturer. In this sampling method, the sample respondents are chosen based on their proximity to the survey desk and willingness to participate in the research.

**Judgemental/purposive sampling:**The judgemental or purposive sampling method is a method of developing a sample purely on the basis and discretion of the researcher purely, based on the nature of the study along with his/her understanding of the target audience. This sampling method selects people who only fit the research criteria and end objectives, and the remaining are kept out.

For example, if the research topic is understanding what University a student prefers for Masters, if the question asked is “Would you like to do your Masters?” anything other than a response, “Yes” to this question, everyone else is excluded from this study.

**Snowball sampling:**Snowball sampling or chain-referral sampling is defined as a non-probability sampling technique in which the samples have rare traits. This is a sampling technique in which existing subjects provide referrals to recruit samples required for a research study.

For example, while collecting feedback about a sensitive topic like AIDS, respondents aren’t forthcoming with information. In this case, the researcher can recruit people with an understanding or knowledge of such people and collect information from them or ask them to collect information.

**Quota sampling:**Quota sampling is a method of collecting a sample where the researcher has the liberty to select a sample based on their strata. The primary characteristic of this method is that two people cannot exist under two different conditions. For example, when a shoe manufacturer would like to understand millennials’ perception of the brand with other parameters like comfort, pricing, etc. It selects only females who are millennials for this study as the research objective is to collect feedback about women’s shoes.

**How to determine a Sample Size**

As we have learned above, the right sample size determination is essential for the success of data collection in a market research study. But is there a correct number for the sample size? What parameters decide the sample size? What are the distribution methods of the survey?

To understand all of this and make an informed calculation of the right sample size, it is first essential to understand four important variables that form the basic characteristics of a sample. They are:

**Population size:**The population size is all the people that can be considered for the research study. This number, in most cases, runs into huge amounts. For example, the population of the United States is 327 million. But in market research, it is impossible to consider all of them for the research study.**The margin of error (confidence interval):**The margin of error is depicted by a percentage that is a statistical inference about the confidence of what number of the population depicts the actual views of the whole population. This percentage helps towards the statistical analysis in selecting a sample and how much sampling error in this would be acceptable.**Confidence level:**This metric measures where the actual mean falls within a confidence interval. The most common confidence intervals are 90%, 95%, and 99%.**Standard deviation:**This metric covers the variance in a survey. A safe number to consider is .5, which would mean that the sample size has to be that large.

**Calculating Sample Size**

To calculate the sample size, you need the following parameters.

- Z-score: The Z-score value can be foundhere.
- Standard deviation
- Margin of error
- Confidence level

To calculate use the sample size, use this formula:

Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Consider the confidence level of 90%, standard deviation of .6 and margin of error, +/-4%

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603

603 respondents are needed and that becomes your sample size.

Try our sample size calculator to give population, margin of error calculator, and confidence level.

**Sampling Advantages**

As shown above, there are many advantages to sampling. Some of the most significant advantages are:

**Reduced cost & time:**Since using a sample reduces the number of people that have to be reached out to, it reduces cost and time. Imagine the time saved between researching with a population of millions vs. conducting a research study using a sample.**Reduced resource deployment:**It is obvious that if the number of people involved in a research study is much lower due to the sample, the resources required are also much less. The workforce needed to research the sample is much less than the workforce needed to study the whole population.**Accuracy of data:**Since the sample indicates the population, the data collected is accurate. Also, since the respondent is willing to participate, the survey dropout rate is much lower, which increases the validity and accuracy of the data.**Intensive & exhaustive data:**Since there are lesser respondents, the data collected from a sample is intense and thorough. More time and effort are given to each respondent rather than collecting data from many people.**Apply properties to a larger population:**Since the sample is indicative of the broader population, it is safe to say that the data collected and analyzed from the sample can be applied to the larger population, which would hold true.

To collect accurate data for research, filter bad panelists, and eliminate sampling bias by applying different control measures. If you need any help arranging a sample audience for your next market research project, contact us at sales@questionpro.com. We have more than 22 million panelists across the world!

## Conclusion

In conclusion, a sample is a subset of a population that is used to represent the characteristics of the entire population. Sampling is essential in research and data analysis to make inferences about a population based on a smaller group of individuals. There are different types of sampling, such as probability sampling, non-probability sampling, and others, each with its own advantages and disadvantages.

Choosing the right sampling method depends on the research question, budget, and resources is important. Furthermore, the sample size plays a crucial role in the accuracy and generalizability of the findings.

This article has provided a comprehensive overview of the definition, types, formula, and examples of sampling. By understanding the different types of sampling and the formulas used to calculate sample size, researchers and analysts can make more informed decisions when conducting research and data analysis.

Sampling is an important tool that enables researchers to make inferences about a population based on a smaller group of individuals. With the right sampling method and sample size, researchers can ensure that their findings are accurate and generalizable to the population.

Utilize one of QuestionPro’s many survey questionnaire samples to help you complete your survey.

When creating online surveys for your customers, employees, or students, one of the biggest mistakes you can make is asking the wrong questions. Different businesses and organizations have different needs required for their surveys.

If you ask irrelevant questions to participants, they’re more likely to drop out before completing the survey. A questionnaire sample template will help set you up for a successful survey.

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## FAQs

### Sample: Definition, Types, Formula & Examples | QuestionPro? ›

Sampling means **selecting the group that you will actually collect data from in your research**. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

**What is sampling and its types with examples? ›**

Sampling means **selecting the group that you will actually collect data from in your research**. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

**What is sample definition and example in statistics? ›**

A sample is defined as **a smaller and more manageable representation of a larger group**. A subset of a larger population that contains characteristics of that population. A sample is used in statistical testing when the population size is too large for all members or observations to be included in the test.

**What are the 5 main types of sampling? ›**

**Methods of sampling from a population**

- Simple random sampling. ...
- Systematic sampling. ...
- Stratified sampling. ...
- Clustered sampling. ...
- Convenience sampling. ...
- Quota sampling. ...
- Judgement (or Purposive) Sampling. ...
- Snowball sampling.

**What is sample size definition and formula? ›**

In statistics, the sample size is **the measure of the number of individual samples used in an experiment**. For example, if we are testing 50 samples of people who watch TV in a city, then the sample size is 50. We can also term it Sample Statistics.

**What is a simple example of sampling? ›**

An example of a simple random sample would be **the names of 25 employees being chosen out of a hat from a company of 250 employees**. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.

**What is the formula for stratified sampling? ›**

For example, if the researcher wanted a sample of 50,000 graduates using age range, the proportionate stratified random sample will be obtained using this formula: **(sample size/population size) × stratum size**.

**What are 3 examples of sample statistics? ›**

A sample statistic (or just statistic) is defined as any number computed from your sample data. Examples include the **sample average, median, sample standard deviation, and percentiles**.

**What is the sample meaning formula? ›**

FAQs on Sample Mean Formula

The general formula for calculating the sample mean is given by **x̄ = ( Σ xi ) / n**. Here, x̄ represents the sample mean, xi refers all X sample values and n stands for the number of sample terms in the data set.

**What are sample types in statistics? ›**

There are five types of sampling: **Random, Systematic, Convenience, Cluster, and Stratified**.

### What are the types of sample? ›

Probability Sampling methods are further classified into different types, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling.

**What are the four basic sampling? ›**

There are four basic types of probability sampling: **simple random sampling, stratified random sampling, systematic random sampling, and cluster random sampling**.

**What is 5 simple random sampling is a sampling method? ›**

Simple random sampling is **a type of probability sampling in which the researcher randomly selects a subset of participants from a population**. Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

**What type of formula is sample size? ›**

The following simple formula would be used for calculating the adequate sample size in prevalence study (4); **n = Z 2 P ( 1 - P ) d 2** Where n is the sample size, Z is the statistic corresponding to level of confidence, P is expected prevalence (that can be obtained from same studies or a pilot study conducted by the ...

**What is the formula for sample size formula? ›**

Its equation can derive using population size, the critical value of the normal distribution, sample proportion, and margin of error. Standard deviation divided by the sample size, multiplying the resultant figure with the critical factor. **Margin of Error = Z * ơ / √n**read more.

**What is simple random sampling formula examples? ›**

For example, **if you randomly select 1000 people from a town with a population of 100,000 residents, each person has a 1000/100000 = 0.01 probability**. That's a simple calculation requiring no additional knowledge about the population's composition. Hence, simple random sampling.

**What is the best example of a sample? ›**

A sample is just a part of a population. For example, let's say your population was every American, and you wanted to find out how much the average person earns. Time and finances stop you from knocking on every door in America, so you choose to ask 1,000 random people. This one thousand people is your sample.

**What is the formula for sample size for a small population? ›**

The Slovin's Formula is given as follows: **n = N/(1+Ne ^{2})**, where n is the sample size, N is the population size and e is the margin of error to be decided by the researcher.

**What is an example of cluster sampling? ›**

An example of single-stage cluster sampling – **An NGO wants to create a sample of girls across five neighboring towns to provide education**. Using single-stage sampling, the NGO randomly selects towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.

**What is an example of systematic random sampling? ›**

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by **selecting every 15th person on a list of the population**. If the population is in a random order, this can imitate the benefits of simple random sampling.

### What is an example of a stratified random sample? ›

But the most common type is probably proportional stratified random sampling, where a population divides into strata, and then the random sample is taken from each stratum in proportion to its size. For example, **if the entire population is 60% female and 40% male, then the sample would be 60% female and 40% male**.

**What are 3 examples of sample vs population? ›**

Population | Sample |
---|---|

Undergraduate students in the Netherlands | 300 undergraduate students from three Dutch universities who volunteer for your psychology research study |

All countries of the world | Countries with published data available on birth rates and GDP since 2000 |

**What is the rule of 3 sampling? ›**

In statistical analysis, the rule of three states that **if a certain event did not occur in a sample with n subjects, the interval from 0 to 3/n is a 95% confidence interval for the rate of occurrences in the population**. When n is greater than 30, this is a good approximation of results from more sensitive tests.

**How many types of samples are there and what are they? ›**

There are **two main types of sampling: probability sampling and non-probability sampling**. The main difference between the two types of sampling is how the sample is selected from the population.

**What is the formula for two sample means? ›**

The two sample t-statistic calculation depends on given degrees of freedom, **df = n1 + n2 – 2**. If the value of two samples t-test for independent samples exceeds critical T at alpha level, then you can reject null hypothesis that there is no difference between two data sets (H0).

**What is the formula for mean for sample and population? ›**

The formula used for the sample mean evaluation is: **x̄ =∑x _{i} /n**. The formula used for the population mean calculation is: μ=∑X / N.

**What is the formula of sample in research? ›**

If there are N units in the population and n units are to be selected, then **R = N/n** (the R is known as the sampling interval). The first number is selected at random out of the remainder of this R (Sampling Interval) to the previous selected number.

**What are the 4 types of sampling PDF? ›**

This article review the sampling techniques used in research including Probability sampling techniques, which include simple random sampling, systematic random sampling and stratified random sampling and Non-probability sampling, which include quota sampling, self-selection sampling, convenience sampling, snowball ...

**What is the most common type of sampling? ›**

**How many types of sample collection are there? ›**

**Three** popular methods of blood collection or sampling are: Arterial Sampling. Venipuncture Sampling. Fingerstick Sampling.

### Why do we use samples? ›

Samples are used **to make inferences about populations**. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

**What are the three elements of sampling? ›**

In other words, the sampling process involves three main elements – **selecting the sample, collecting the information, and also making inferences about the population**.

**What are the 4 sampling strategies in research? ›**

Four main methods include: 1) **simple random, 2) stratified random, 3) cluster, and 4) systematic**.

**What are the 5 types of non random sampling? ›**

There are five types of non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master's level: quota sampling, convenience sampling, purposive sampling, self-selection sampling and snowball sampling.

**What is simple sample vs simple random sample? ›**

A random sample only requires that every item in a population has a greater than zero chance of being drawn. Unlike in a simple random sample, that probability may or may not be equal for every item. In simple random sampling, every element of a 1,000-item set has an equal probability of one in 1,000 to be selected.

**What are the types of probability sampling? ›**

**Simple random sampling, stratified sampling, cluster sampling, and systematic sampling** are all types of probability sampling.

**What are 3 factors that determine sample size? ›**

In general, three or four factors must be known or estimated to calculate sample size: (1) the effect size (usually the difference between 2 groups); (2) the population standard deviation (for continuous data); (3) the desired power of the experiment to detect the postulated effect; and (4) the significance level.

**What is the formula for sampling ratio? ›**

Sampling ratio is **size of sample divided by size of population**.

**Why is sample size formula important? ›**

The sample size for a study needs to be estimated at the time the study is proposed; **too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical**. The necessary sample size can be calculated, using statistical software, based on certain assumptions.

**What is the formula for population size? ›**

The population size estimate is obtained by dividing the number of individuals receiving a service or the number of unique objects distributed (M) by the proportion of individuals in a representative survey who report receipt of the service or object (P).

### Which is the best formula for sampling? ›

The Slovin's Formula is given as follows: **n = N/(1+Ne ^{2})**, where n is the sample size, N is the population size and e is the margin of error to be decided by the researcher.

**What is the purpose of sampling? ›**

The aim of sampling is **to approximate a larger population on characteristics relevant to the research question, to be representative so that researchers can make inferences about the larger population**.

**What are the examples of sampling and population? ›**

Population | Sample |
---|---|

Undergraduate students in the Netherlands | 300 undergraduate students from three Dutch universities who volunteer for your psychology research study |

All countries of the world | Countries with published data available on birth rates and GDP since 2000 |

**What is the difference between stratified and systematic sampling? ›**

Difference between stratified sampling and systematic sampling? **In systematic sampling, the list of elements is "counted off".** **That is, every kth element is taken.** **Stratified sampling also divides the population into groups called strata**.

**What are the 3 factors of sampling? ›**

In general, three or four factors must be known or estimated to calculate sample size: (1) the effect size (usually the difference between 2 groups); (2) the population standard deviation (for continuous data); (3) the desired power of the experiment to detect the postulated effect; and (4) the significance level.

**What are the most common sampling methods? ›**

Three main strategies are identified for sampling. They include **selective sampling, volume-reduced sampling and bulk sampling**.

**What are the types of sampling? ›**

Sampling in market action research is of two types – probability sampling and non-probability sampling. Let's take a closer look at these two methods of sampling. Probability sampling: Probability sampling is a sampling technique where a researcher selects a few criteria and chooses members of a population randomly.

**What is a sampling technique? ›**

In Statistics, the sampling method or sampling technique is **the process of studying the population by gathering information and analyzing that data**. It is the basis of the data where the sample space is enormous. There are several different sampling techniques available, and they can be subdivided into two groups.

**What are the principles of sampling? ›**

(a) **The sample, that is, the selection of items from the parent population, is selected randomly**. (b) The sample size, that is, the number of items in the sample is large enough to avoid sampling fluctuation. (c) Over a long period of time, sampling results will be true on average.

**Why is sampling techniques important? ›**

The primary goal of sampling is to create a representative sample, one in which the smaller group (sample) accurately represents the characteristics of the larger group (population). If the sample is well selected, the sample will be generalizable to the population.

### What are the essential of a good sample? ›

Answer: The essentials of sampling are: **The sample must truly represent the population**. Its size must be adequate. You must select the sample randomly and independently.

**What are examples of cluster sampling? ›**

An example of single-stage cluster sampling – **An NGO wants to create a sample of girls across five neighboring towns to provide education**. Using single-stage sampling, the NGO randomly selects towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.

**What is progressive sampling? ›**

Progressive Sampling (PS) **starts with a small data sample from the full dataset and use progressively larger samples until the model accuracy cannot increase substantially**. PS techniques attempts to efficiently maximize model accuracy by using growing the sample size.

**Why simple random sampling is used? ›**

Simple random sampling is used **to make statistical inferences about a population**. It helps ensure high internal validity: randomization is the best method to reduce the impact of potential confounding variables.