Sample: Definition, Types, Formula & Examples | QuestionPro (2023)

Sample: Definition, Types, Formula & Examples | QuestionPro (1)

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

  1. What is a sample?
  2. Types of samples: Sample selection methodologies with examples
    1. Probability sampling methodologies with examples
    2. Non-probability sampling methodologies with examples
  3. How to determine a sample size
  4. Calculating sample size
  5. Sampling advantages

What is a Sample?

Definition: 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. 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, an in-depth research study must be conducted.

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.

(Video) Sample size calculation for comparing sample means from two paired samples

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.

Select your respondents

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.

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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 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 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:

(Video) 5 4 Power Calculations for difference of two means

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 respondents are needed and that becomes your sample size.

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

Sampling Advantages

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

(Video) Sampling 03: Stratified Random Sampling

  • 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 with arranging a sample audience for your next market research project, get in touch with us at [emailprotected] We have more than 22 million panelists across the world!

Select your respondents

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. It’s important to choose the right sampling method depending on the research question, budget, and resources. 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.



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 are the 4 sampling strategies? ›

Four main methods include: 1) simple random, 2) stratified random, 3) cluster, and 4) systematic. Non-probability sampling – the elements that make up the sample, are selected by nonrandom methods. This type of sampling is less likely than probability sampling to produce representative samples.

What are types of samples? ›

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 best definition of sampling? ›

Sampling is the process of selecting a number of cases from all the cases in a particular group or universe. Context: Sampling is the research strategy of collecting data from a part of a population with a view to drawing inferences about the whole.

What are the 4 types of random sampling? ›

There are four primary, random (probability) sampling methods – simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

What are the 3 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.

What is the formula of sample mean? ›

How to calculate the sample mean. Calculating sample mean is as simple as adding up the number of items in a sample set and then dividing that sum by the number of items in the sample set. To calculate the sample mean through spreadsheet software and calculators, you can use the formula: x̄ = ( Σ xi ) / n.

What is a sample distribution example? ›

The sampling distribution of a proportion is when you repeat your survey or poll for all possible samples of the population. For example: instead of polling asking 1000 cat owners what cat food their pet prefers, you could repeat your poll multiple times.

What is called a sample? ›

What Is a Sample? A sample refers to a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations.

What are the sample data? ›

A sample data set contains a part, or a subset, of a population. The size of a sample is always less than the size of the population from which it is taken. [Utilizes the count n - 1 in formulas.] Example: The sample may be "SOME people living in the US."

What are the two types of sampling? ›

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.

What is the formula of random sampling? ›

The Formula of Random Sampling

(N-n/N-(n-1)). Here P is a probability, n is the sample size, and N represents the population. Now if one cancels 1-(N-n/n), it will provide P = n/N. Moreover, the chance of a sample getting selected more than once is needed: P = 1-(1-(1/N)) n.

What are examples of random sampling methods? ›

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 an example of systematic random sample? ›

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.

How to sample data? ›

The most straightforward way to sample data is with simple random sampling. Essentially, the subset is built of observations that were chosen from a larger set purely by chance; Each observation has the same chance of being selected from the larger set. Simple random sampling is extremely simple and easy to implement.

What are the golden rules of sampling? ›

Good Sampling Practice

The “Golden Rules” for sub-sampling put forth by Allen [1] simply state that the sample(s) should be taken when the powder is in motion (i.e. a powder stream), and the entire cross section of the entire stream shall be sampled many times.

What is the simplest 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.

What is the best type of sampling? ›

Random samples are the best method of selecting your sample from the population of interest. The advantages are that your sample should represent the target population and eliminate sampling bias. The disadvantage is that it is very difficult to achieve (i.e. time, effort and money).

What is the most easy sampling method? ›

Simple random sampling is considered the easiest method of probability sampling. To perform simple random sampling, all a researcher must do is ensure that all members of the population are included in a master list, and that subjects are then selected randomly from this master list.

What are the types of sampling errors? ›

In general, sampling errors can be placed into four categories: population-specific error, selection error, sample frame error, or non-response error. A population-specific error occurs when the researcher does not understand who they should survey.

How many types of sample preparation methods are there? ›

The major sample preparation techniques that are amenable to automation are solid-phase extraction, LC, dialysis, microwave sample preparation, flow injection analysis, and segmented flow analysis.

What is 3 class sampling plan? ›

A Three-class sampling plan is defined by (n,c,m,M) with an additional specification limit M> m; the lot is also rejected if at least one of the n measured log-concentrations is larger than M. A Three-class sampling plan protects better against unacceptable lots than the underlying Two-class sampling plan.

What is the formula of mean in short method? ›

To calculate mean deviation about mean by shortcut method, *First take an appropriate 'Assumed Mean' A. *calculate the sum of the frequencies, ∑i=1nfi . *calculate ∑i=1nfidi where, di=xi−A . *calculate mean as xˉ=A+∑fi∑fidi×h where h = the class width =upperclasslimit−lowerclasslimit of the frequency distribution.

What is a formula for the mean of sampling distribution? ›

For samples of any size drawn from a normally distributed population, the sample mean is normally distributed, with mean μX=μ and standard deviation σX=σ/√n, where n is the sample size.

What is the formula for calculating mean in statistics? ›

It's obtained by simply dividing the sum of all values in a data set by the number of values.

What are 5 random sampling techniques? ›

There are five types of sampling: Random, Systematic, Convenience, Cluster, and Stratified. Random sampling is analogous to putting everyone's name into a hat and drawing out several names. Each element in the population has an equal chance of occuring.

What is a sample size in research? ›

Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions.

What is an example of a sample population? ›

Population and Sample Examples

All the people who have the ID proofs is the population and a group of people who only have voter id with them is the sample. All the students in the class are population whereas the top 10 students in the class are the sample.

What are some examples of sampling units? ›

For example, if you were conducting research using a sample of university students, a single university student would be a sampling unit. Another example of a sampling unit could be if you were conducting online research with 50 households, one household would be a singular sampling unit.

What are types of sampling distribution? ›

Sampling distribution of the mean, sampling distribution of proportion, and T-distribution are three major types of finite-sample distribution. The central limit theorem states how the distribution still remains normal and almost accurate with increasing sample size.

What is simple sampling example? ›

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 random sampling and its 4 types? ›

There are four primary, random (probability) sampling methods – simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

What is simple random sampling and its types? ›

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 is sampling and why is it used? ›

Sampling is a tool that is used to indicate how much data to collect and how often it should be collected. This tool defines the samples to take in order to quantify a system, process, issue, or problem. To illustrate sampling, consider a loaf of bread. How good is the bread?

How do you use sample and example? ›

“Sample”- Learn the Difference. The word example is used to mention an illustration, in support of a claim. The word sample is used to denote a specimen or model.

What is an example of sample data? ›

The data are the number of books students carry in their backpacks. You sample five students. Two students carry three books, one student carries four books, one student carries two books, and one student carries one book. The numbers of books (three, four, two, and one) are the quantitative discrete data.

What is the sample size formula? ›

The formula for determining sample size to ensure that the test has a specified power is given below: where α is the selected level of significance and Z 1-α /2 is the value from the standard normal distribution holding 1- α/2 below it. For example, if α=0.05, then 1- α/2 = 0.975 and Z=1.960.

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 is systematic example? ›

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 stratified sample in statistics? ›

What is stratified sampling? In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment). Once divided, each subgroup is randomly sampled using another probability sampling method.

What are the 3 main types of non random sampling? ›

In a non-probability sample, some members of the population, compared to other members, have a greater but unknown chance of selection. There are five main types of non-probability sample: convenience, purposive, quota, snowball, and self-selection.

Why is a sample important? ›

Studies are conducted on samples because it is usually impossible to study the entire population. Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well. The sample must therefore be representative of the population.

What are sample means used for? ›

A sample mean is an average of a set of data . The sample mean can be used to calculate the central tendency, standard deviation and the variance of a data set. The sample mean can be applied to a variety of uses, including calculating population averages.


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