Non-Probability Sampling

Updated on April 4, 2024
Article byAswathi Jayachandran
Edited byAswathi Jayachandran
Reviewed byDheeraj Vaidya, CFA, FRM

What Is Non-Probability Sampling?

Non-probability Sampling is a method used by researchers to select units from a population based on specific criteria. Researchers select specific populations based on factors like location or age rather than random selection. The decision is made based on convenience or the researcher’s assessment.

Non-Probability Sampling Process

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It is fast, simple, and affordable to collect data with non-probability sampling since it does not require a full survey frame. Researchers use this approach to find volunteers with comparable features or when time or money is a constraint in their study. It is especially helpful in situations where it is challenging to define or reach the target group.

Key Takeaways

  • Non-probability sampling is a method in which researchers choose samples based on subjective assessment rather than by chance. In this approach, not every member of the population has an equal opportunity to participate.
  • Subjective approach, convenience, purposefulness and non-inclusiveness are some of the characteristics of non-probability sampling.
  • Non-probability sampling is particularly useful for exploratory research, such as pilot surveys, when there are constraints on time or finances that prevent a larger sample size.
  • Non-probability sampling methods include convenience sampling, consecutive sampling, voluntary sampling, purposive sampling, snowball sampling, and quota sampling.

Non-Probability Sampling Explained

Non-probability sampling is a method where researchers select samples not by chance but rather by subjective assessment. Not every member of the population has an equal opportunity to participate in this strategy. The approach is popular for qualitative research and mostly depends on the researchers’ experience.

It is especially helpful for exploratory research, such as pilot surveys, when time or financial constraints prevent the survey from being sent to a larger sample size. When population factors are unknown or not individually identifiable, non-probability sampling is also utilized. A comprehensive and current list of the population under study is necessary for probability sampling, although non-probability samples usually lack this information. In situations where a comprehensive population list is not easily accessible, non-probability techniques are employed.

It is also utilized when there is minimal or no need to reach the entire population. It facilitates the collection of opinions from a certain demographic or niche based on attributes or location. A small-scale pilot or exploratory study in a target market may be helpful in ascertaining the viability of novel goods and services. When resources are scarce, non-probability sampling can be used to allocate resources economically. It is especially helpful for members of marginalized groups who are not often well-represented in huge communities or who go unnoticed.

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Some of the characteristics of non probability sampling procedure are given below:

  • Subjective selection: In this method, rather than choosing participants at random, the researcher chooses them based on their judgment, level of experience, or convenience.
  • Non-inclusive: Since randomization is not used in non-probability sampling, the sample produced may not represent the sample population.
  • Sampling for Convenience: When conducting a study, researchers often select participants based on their accessibility or availability.
  • Purposeful Sampling: The selection of participants is done according to certain standards that are consistent with the goals or research issue.


There are different non-probability sampling methods, and they are given as follows:

Probability Sampling Methods

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#1 – Convenience Sampling

Convenience sampling is a cost-effective and quick non-probability sampling method. Researchers use it to select individuals for their studies due to their convenience and availability. This method can be used in schools, workplaces, or neighborhoods, where individuals are easily accessible and easily locatable for testing purposes.

#2 – Consecutive Sampling

Consecutive research involves selecting a small sample, conducting the study, analyzing the results, and then gathering more samples if needed. For instance, researchers may conduct surveys in public locations like malls or grocery stores, obtaining results from a specific population. They may then move to another location to gather more data.

#3 – Voluntary Sampling

Voluntary sampling is a method where researchers request volunteers for their studies, often through in-person interactions, email, or marketing advertisements. This method is beneficial for quickly obtaining data, as it allows individuals within a known population to participate. For instance, businesses may ask customers to complete surveys about their dining experiences.

#4 – Purposive sampling

Non probability purposive sampling involves researchers using their judgment to recruit participants accurately representing the target population. This method is beneficial when the population is not common or difficult to find, such as in a study on third-aboriginal tribal students. Non-probability sampling purposive sampling allows for a more accurate representation of the target population. It is also known as judgment sampling.

#5 – Snowball Sampling

In snowball sampling, researchers can increase the sample size by having participants reach out to friends and family who meet the study’s eligibility requirements. It is done by the use of participants in a study to find more volunteers. Although its reliance on referrals can make it slow, this approach is useful for researching neglected groups.

#6 – Quota Sampling

Quota sampling is a method used by researchers to select a specific sample of a specific character within a population. Groups are based on factors such as age or gender, as well as by variables like income, state, and education level. Samples are then taken from each category.


Let us look into a few examples to understand the concept better:

Example #1

Suppose Dan, a researcher, uses non-probability sampling to gather data for making personal financial and investment decisions.

He selects individuals from his friends, family, and colleagues who he believes can provide valuable insights into investment strategies. He selects a set of people who are working and make investments. Then, he studies them by taking into consideration their age, gender, debts, income etc. and makes choices.

Example #2

Amazon conducts customer satisfaction surveys through email or on its website, targeting a selected group of customers who recently made a purchase or interacted with its platform. This method provides insights into customer experiences, preferences, and suggestions for improvement. However, the survey respondents may not represent the entire customer population, potentially introducing biases. Despite this, Amazon uses this method to understand customers and make decisions to improve their services and offerings.

Amazon has hired Qualtrics to survey how users experience the news. The survey had questions related to customers’ use of the company’s Fire TV video hardware. Fire TV already supports numerous news apps. Amazon also mentioned that they initially tried to build a pay TV service but ultimately backed away from it. It targets a selection group (its users), and the company develops its business strategies through the data obtained.

Advantages And Disadvantages

Some of the advantages and disadvantages of non-probability sampling are given as follows:


  • Quick, affordable, and requires minimal research
  • Aids in geographic restrictions being removed by using online platforms for non-probability sampling
  • Provides a starting point for quick hypotheses
  • Population surveying can be done until the target data or sample size is reached.
  • Makes it possible to engage with underrepresented or specialty populations through deviant sampling.
  • Enables opinions on current affairs and issues to be gathered quickly.


  • Surveys could not be entirely representative of the general public because of their accessibility or convenience.
  • Surveys may not be entirely representative of the general population due to convenience or accessibility.
  • A margin of error that represents the opinions of the entire population may be difficult for researchers to compute.
  • Like any other data collecting, it is possible to have sampling bias and surveying disadvantages.
  • Possible sampling bias results from intentional selection.
  • Since the constant population borders have not been measured, sample sizes may be ambiguous.
  • The researcher’s personal opinions may influence the sample.

Frequently Asked Questions (FAQs)

1. Can non-probability sampling be used in quantitative research?

Non-probability sampling can be utilized in quantitative research; however, it carries the risk of introducing biases and limiting generalization due to the absence of randomization. This type of sampling is more commonly employed in exploratory or qualitative studies.

2. Why is probability sampling preferred to non-probability sampling?

Probability sampling is preferred over non-probability sampling because it enables statistical inference. It is accurate, ensures sample representativeness, minimizes biases, and provides a foundation for estimating population parameters and drawing generalizations.

3. How is probability sampling different from non-probability sampling?

Probability sampling differs from non-probability sampling in that it involves random selection, guaranteeing that every member of the sample population has a known chance of being included. On the other hand, non-probability sampling relies on subjective judgment or convenience, which can result in biases and limited generalizability.

4. What are the similarities between probability sampling and non-probability sampling?

Probability and nonprobability samplinshare the goal of selecting individuals from a population for research purposes. They involve the sampling process and are used to collect data for research studies. However, probability and nonprobability sampling differ in terms of randomization and representativeness in their approaches and methodologies.

This article has been a guide to what is Non-Probability Sampling. Here, we explain its types, examples, advantages, disadvantages, and characteristics. You may also find some useful articles here –

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