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What is sampling?

The Essential Guide to Understanding Sampling in Research

Abstract image about survey sampling
Published: 16th March 2024

What is sampling in research?

In research, sampling is a crucial technique that involves selecting a subset of individuals from a larger population to participate in a study. This approach is often necessary because it's impractical, and sometimes impossible, to study an entire population directly due to time, cost, or logistical constraints. By carefully choosing a representative sample, researchers can draw conclusions and make inferences about the population as a whole.

Why Sampling is Important:

  1. Cost-Effective: Sampling reduces the cost of research by limiting the number of participants.
  2. Time-Saving: It enables quicker data collection compared to studying a whole population.
  3. Feasibility: In many cases, accessing the entire population is not feasible.
  4. Accuracy: With proper sampling techniques, you can achieve highly accurate results.

Types of Sampling:

Samples can be obtained in a variety of ways, and can include:

  1. Haphazard: haphazard sampling refers to the selection of a sample of participants using ‘trial and error’ or ‘hit and miss’ approaches. Haphazard sampling does not rely on any specific criteria or approaches, and often means that your results will be unpredictable and prone to error because there is no systematic approach used.
  2. Purposive: purposive sampling is the selection of a non-random sample, whereby participants are specifically chosen because of a particular reason (i.e.they were chosen on purpose because they meet a certain criteria). While purposive samples are advantageous because they focus on a specific group, if the researcher makes an error in judgement about whether an individual meets criterion for inclusion this will influence the results.
  3. Convenience: convenience sampling is another form of non-random sampling, where participants are chosen because of convenience-related reasons, such as accessibility or availability.Such samples are advantageous because participants are often readily available and easy to access, however this approach often increases the risk of bias in the results.

While each of the above approaches have their place, a common limitation to all three approaches is that they are quite subjective in nature, and heavily rely on researcher discretion to determine them. Another limitation is that since these approaches are targeted (i.e. non-random), this raises questions about whether we can generalise our findings to the broader population of interest. While how you sample will be based on your specific research question and study design, generally the preferred method of sampling is the probability approach.

Probability Sampling:

Probability sampling, also referred to as random sampling,is the independent and random selection of participants based on probability theory, in that it is controlled by chance alone. Sampling based on probability is advantageous because it increases the likelihood of obtaining a sample that is more representative of the population you are interested in.For a sample to be genuinely random, each participant drawn from the population of interest must have an equal chance of being selected, and one participant being selected must occur independently of any other participant being selected.

There are several subtypes of probability sampling, and include systematic, simple random, stratified random and cluster samples. We will explore each of these types of samples using examples based on a controversial topic: does pineapple belongs on pizza?

Systematic Samples:

A systematic sample is a type of probability sampling, however systematic samples are not random. In systematic sampling, a rule for selecting participants is pre-determined and applied. A common form of this is to select every ‘nth’ person to be part of the sample.

For our pizza example, suppose you wanted to select a sample of 25 guests out of 100 who are attending a pizza party, by surveying every 4th guest who arrives. Each guest is allocated a number from 1 to 100, and as they enter you ask every 4th person whether pineapple belongs on pizza. By choosing every 4th person, you obtain a probability sample because 25% of guests have been selected, however this sample is not random because any guest who was not number 4, 8, 12 and so on had zero chance of being selected.

Simple Random Samples:

Simple random samples are random samples selected from the population of interest where each participant in the sample is equally likely to be selected compared to the next participant. Simple random selection is often used when the population you are sampling is relatively homogenous, or similar. Selection from this population is made based on entirely randomised methods, such as by lottery.

Continuing our pizza example, suppose that most of the guests at the pizza party are pineapple lovers, and each guest is to be allocated a number from 1 to 100. While your colleague is handing out numbers to guests, you hop online and locate a random number generator. Once each guest has a number, you generate a random number between 1 and 100, and you ask the guest who corresponds with the generated number whether pineapple belongs on pizza. You repeat this 25 times until you have a sample of 25 guests out of the 100 attending. You can then collate the answers obtained and see what the consensus amongst pineapple lovers is about whether it belongs on pizza. This is a probability sample because 25% of guests have been selected, and it is random because there is an equal chance of being selected at random. Simple random selection was used because the sample was relatively homogenous, in that most of the guests are known lovers of pineapple.

Stratified Random Samples:

A stratified random sample is a random sample where two or more groups are represented from your population of interest. Stratified random sampling is more often used when the population you are sampling is relatively heterogeneous, or there are notable subgroups present. This involves dividing your population into the smaller groups and then randomly selecting a sample from each - in essence, you are treating it as if there are two populations. Common examples include stratifying by age, sex or ethnicity.

In relation to our pizza example, let’s assume that at our pizza party there are 55 females and 45 males, and you have reason to believe that both sexes will respond differently to the question of whether pineapple belongs on pizza. To be representative, and for a sample of 25%, you determine that you will need to survey 14 females and 11 males. You randomly allocate each female a number from 1 to 55, and each male a number from 1 to 45. Starting with the female group, as before you hop onto the random number generator and generate a number between 1 and 55, and ask the female guest who corresponds with the generated number whether pineapple belongs on pizza. You repeat this 14 times until you have completed your female sample. Once this has been completed, you repeat the entire process for the males.We can then compare the responses of females to males to see if one sex prefers pineapple on pizza more than the other sex. Like with simple random sampling, this example is a probability sample because 25% of guests from each subgroup have been selected, and it is random because there is an equal chance of being selected at random. Stratified random selection was used because the sample was heterogenous, in that there were males and females.

Random Cluster Sampling:

Cluster sampling occurs when groups of the population of interest are selected at random. Cluster sampling often occurs in two stages – in the first stage, the population of interest is broken down into the known clusters. Then, in the second stage, multiple clusters are randomly selected and participants within each of the chosen cluster are randomly chosen to comprise the final sample.

At our pizza party of 100 guests, let’s assume that there are clusters of guests who prefer different types of pizza bases – thin, medium, thick, cheese crust, hot-dog crust and gluten-free. You allocate each cluster a number from 1 to 6, and using a random number generator, determine that for a sample of approximately 25% you will be sampling guests from clusters who prefer thin and cheese crust pizza bases. You would then follow the same procedure as stratified random sampling, as you now have two groups to sample from. Each individual from the thin crust and cheese crust groups are allocated numbers, and 25% of each group are randomly selected via random number generation and asked whether they think pineapple belongs on pizza. Like before, this is a probability sample because 25% of guests from each cluster were selected, and it is random because there is an equal chance of being selected at random from each cluster. However, cluster sampling is not necessarily as representative as stratified random sampling because not all clusters were examined.

Common Questions About Sampling:

What is the difference between stratified and cluster sampling?

When conducting research, choosing the right sampling method is crucial. Stratified and cluster sampling are both probability sampling techniques that serve different purposes based on the study's requirements.

Stratified Sampling Cluster Sampling
Definition Divides the population into smaller groups (strata) based on shared characteristics and samples are drawn from each group. Divides the population into clusters based on a certain criterion, then randomly selects entire clusters to be sampled.
Purpose Ensures representation of key subgroups within the population. Convenience and cost-effectiveness, especially for geographically dispersed populations.
Selection Randomly selects individuals from within each stratum. Randomly selects entire clusters, then surveys all or a random selection of members from those clusters.
Best Used When The research aims to analyze specific subgroups within the population. The population is naturally divided into groups, or when logistical constraints are a concern.

Understanding the distinctions between these sampling methods allows researchers to select the most appropriate approach for their study, ensuring reliable and valid results.

Tips for Choosing Between Stratified and Cluster Sampling

  • Identify the main goal of your research.
  • Consider the structure and distribution of your population.
  • Evaluate the resources available, including time and budget.
  • Assess the need for subgroup analysis in your findings.

For more in-depth explanations and examples:

How do you determine the right sampling method?

Selecting the appropriate sampling method is crucial for the accuracy and reliability of your research findings. The choice depends on several factors including the study objectives, population structure, budget, and time constraints.

Factors to Consider

  • Research Objectives: Clarify what you intend to discover or prove with your study.
  • Population Structure: Understand the characteristics and distribution of your target population.
  • Resources: Assess the available time, budget, and manpower for your project.
  • Accuracy Needs: Determine the level of precision required for your research results.

Sampling Method Overview

Sampling Method When to Use Advantages Disadvantages
Simple Random Sampling When the population is homogenous and easily accessible. Minimizes bias; easy to analyze. Not suitable for large, diverse populations.
Stratified Sampling When analyzing specific subgroups within the population. Ensures subgroup representation. Requires detailed population information.
Cluster Sampling For geographically dispersed populations. Cost-effective; reduces travel/time expenses. Potential for higher sampling error.
Convenience Sampling When time and resources are limited. Easy and inexpensive. May introduce significant bias.

For detailed guidance and examples on selecting the right sampling method, consider consulting:

Choosing the right sampling method is essential for achieving reliable research outcomes. Consider your study's specific needs and constraints carefully before making a decision.

Can Sampling Introduce Bias?

Yes, sampling can introduce bias into research findings. Bias occurs when certain elements of the population are systematically excluded or over-represented in the sample, leading to results that do not accurately reflect the target population. Recognizing and mitigating sampling bias is crucial for maintaining the integrity and reliability of research outcomes.

Common Types of Sampling Bias

Type of Bias Description How to Avoid
Selection Bias Occurs when certain groups are more likely to be selected for the sample due to the sampling method. Use random sampling methods and ensure all segments of the population have an equal chance of being included.
Non-response Bias Arises when a significant number of selected participants refuse to respond or are unreachable. Follow up with non-respondents, offer incentives, and simplify the survey process.
Volunteer Bias Results from individuals volunteering to participate, who may not represent the broader population. Select participants randomly rather than relying on volunteers.

Tips for Reducing Sampling Bias

  • Clearly define your target population.
  • Choose an appropriate sampling method based on the study's needs.
  • Ensure the sample size is sufficient to represent the population.
  • Consider using stratified or cluster sampling to cover diverse subgroups.

Being mindful of sampling bias and implementing measures to avoid it is essential for conducting credible and accurate research.

What is an Example of Sampling?

Sampling is a fundamental research technique used to select a part of a population for study. An example of sampling in research is conducting a survey on dietary habits among teenagers in a city. Given the impracticality of surveying every teenager, researchers might use a stratified random sampling method to ensure representation across different socio-economic backgrounds.

Steps in the Sampling Process:

  1. Identify the total population of teenagers in the city.
  2. Divide the population into strata based on socio-economic status.
  3. Randomly select a proportional number of teenagers from each stratum.
  4. Conduct the survey with the selected sample.

Why This Example Works:

This approach ensures that the survey results can be generalized to the entire teenage population of the city, despite only a subset being studied. By considering socio-economic status, the sample accounts for variations in dietary habits that might be influenced by this factor, thereby reducing sampling bias.

What is the Most Effective Sampling Method?

The most effective sampling method depends on the research objectives, the nature of the population, and the resources available. However, stratified random sampling is often highlighted for its effectiveness in ensuring that all subgroups within a population are adequately represented.

Why Stratified Random Sampling Stands Out:

  • It divides the population into smaller, homogeneous groups (strata) before sampling.
  • Ensures representation of all key subgroups, enhancing the generalizability of the results.
  • Reduces sampling error compared to simple random sampling, especially in heterogeneous populations.

What is the Easiest Sampling Technique?

The easiest sampling technique, particularly in terms of simplicity and accessibility, is convenience sampling. This method involves selecting participants who are readily available and willing to take part in the study, making it a popular choice for preliminary research where speed and efficiency are prioritized over representativeness.

Key Features of Convenience Sampling:

  • Minimal planning and preparation required.
  • Fast and cost-effective data collection.
  • Useful for exploratory research or pilot studies.


While convenience sampling is straightforward, it carries a higher risk of bias and may not accurately represent the larger population. This limitation should be considered when interpreting the results.

Important Notes:

Probability sampling is advantageous because it reduces sampling bias and demonstrates diversity in your sample (and therefore population). Independent and random sampling is also often an assumption of many inferential statistics tests, so if this assumption is not met then certain types of analyses cannot be performed. However, it’s important to remember that while probability sampling is preferred, how you sample your population of interest is dependent on your research question and study design. And, most importantly –yes, pineapple does belong on pizza! ;)

Helpful References:

  1. Australian Bureau of Statistics (2021). Sample Design.
  2. Health Knowledge (2021). Methods of sampling from a population.
  3. Investopedia: Sampling Techniques
  4. Explorable: Introduction to Sampling
  5. SAGE Research Methods: Probability Sampling
  6. Make a Free Survey