Non-Probability Sampling
Non-probability sampling is a sampling method in which participants are selected using non-random criteria, meaning not all members of the population have a known or equal chance of being included in the study.
On this page:
- What is Non-Probability Sampling?
- Types of Non-Probability Sampling
- Sample Size Considerations
- Advantages and Disadvantages
- Non-Probability Sampling in the Age of AI and Digital Research
- When to Use Non-Probability Sampling
| Aspect | Non-Probability Sampling | Probability Sampling |
|---|---|---|
| Selection method | Non-random | Random |
| Chance of selection | Unknown | Known and non-zero |
| Bias level | Higher | Lower |
| Representativeness | More limited | Higher |
| Generalisability | Usually limited | Stronger |
Probability vs Non-Probability Sampling (comparison table)
What is Non-Probability Sampling?
Non-probability sampling prioritises practicality, whereas probability sampling prioritises representativeness.
Non-probability sampling means:
- Participants are selected based on availability or judgement
- Not everyone in the population has a chance to be selected
- Results are quick to obtain but less generalisable
It is mainly used for exploratory and qualitative research.
In non-probability sampling (also known as non-random sampling) not all members of the population have a chance to participate in the study. In other words, this method is based on non-random selection criteria. This is contrary to probability sampling, where each member of the population has a known, non-zero chance of being selected to participate in the study.
Necessity for non-probability sampling can be explained in a way that for some studies it is not feasible to draw a random probability-based sample of the population due to time and/or cost considerations. In these cases, sample group members have to be selected on the basis of accessibility or personal judgment of the researcher. Therefore, the majority of non-probability sampling techniques include an element of subjective judgement. Non-probability sampling is the most helpful for exploratory stages of studies such as a pilot survey.
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Types of Non-Probability Sampling
The following is the list of the most popular non-probability sampling methods and their brief descriptions:
| Non-probability sampling method | Description |
| Judgement Sampling (Purposive Sampling) | Researcher chooses samples purely on the basis of her knowledge and credibility |
| Quota sampling | Researcher chooses sample group members on the basis of their shared traits or characteristics |
| Convenience sampling | Researcher chooses population members that are conveniently available to her. |
| Voluntary response sampling | Respondents voluntarily choose to participate in a study, usually through an online survey |
| Snowball sampling | Initially chosen sample group members help researcher to find new members |
| Consecutive sampling | Researcher selects a sample or group and after data collection and analysis moves to another sample |
Non-probability sampling methods
Sample Size Considerations
The issue of sample size in non-probability sampling is rather ambiguous and needs to reflect a wide range of research-specific factors in each case. Nevertheless, there are some considerations about the minimum sample sizes in non-probability sampling as illustrated in the table below:
| Nature of study | Minimum sample size |
| Semi-structured, in-depth interviews | 5 – 25 |
| Ethnographic | 35 – 36 |
| Grounded theory | 20 – 35 |
| Considering a homogeneous population | 4 – 12 |
| Considering a heterogeneous population | 12 – 30 |
Sizes of non-probability sampling[1]
In qualitative research, sample size is often determined by data saturation rather than fixed numbers.
Advantages and Disadvantages
Practicality is one of the major advantages of non-probability sampling. Researchers can recruit participants more quickly and easily compared to probability sampling methods. Non-probability sampling is also cost-effective because researchers usually do not require complete population databases or sophisticated random selection procedures.
Flexibility can also be mentioned as additional advantage. Researchers can target participants who possess specific knowledge, expertise, experiences, or characteristics relevant to the study. This method is especially useful in exploratory research where the aim is to generate initial insights rather than produce statistically generalisable findings.
Non-probability sampling is also highly valuable when researching:
- specialised professional groups
- vulnerable populations
- hidden communities
- difficult-to-access participants
At the same time, non-probability sampling is not without its limitations. There is higher risk of sampling bias because participants are not selected randomly. Certain groups may become overrepresented while others may not be represented at all. As a result, findings obtained through non-probability sampling are usually less generalisable to wider populations.
Another weakness is the difficulty estimating sampling error statistically because probabilities of selection are unknown. Research credibility may also be questioned if researchers fail to justify:
- participant selection procedures
- sampling rationale
- limitations associated with representativeness
Because of these limitations, researchers must discuss sampling bias and generalisability carefully within the methodology chapter.
Non-Probability Sampling in the Age of AI and Digital Research
AI technologies, online platforms, and digital research environments are significantly increasing the use of non-probability sampling in modern business studies. Researchers now frequently recruit participants through LinkedIn, online communities, social media platforms, email lists, online panels, and AI-assisted survey systems.
Digital technologies make participant recruitment faster, cheaper, and more geographically diverse compared to traditional offline methods. For example, researchers can distribute online surveys globally within minutes through digital platforms.
At the same time, digital non-probability sampling introduces additional methodological concerns related to algorithmic bias, fake accounts, duplicate responses, demographic imbalance, and platform-specific participant behaviour. Surveys distributed mainly through LinkedIn may overrepresent professionals, while social media algorithms may expose surveys only to specific categories of users.
AI-assisted recruitment systems may also unintentionally prioritise particular participant groups depending on platform algorithms and user behaviour patterns. Researchers must therefore critically evaluate representativeness, response authenticity, ethical implications, and digital platform bias when using non-probability sampling in online environments. Despite rapid advances in digital research technologies, careful human judgement remains essential when selecting and evaluating samples.
When to Use Non-Probability Sampling
Non-probability sampling is most suitable when practical constraints or research objectives make probability sampling unsuitable.
You should use non-probability sampling if:
- you are conducting exploratory or qualitative research
- a complete sampling frame is unavailable
- access to participants is limited
- you are working under time or budget constraints
- your study focuses on specialised or hard-to-reach groups
- your goal is to generate insights rather than highly generalisable findings
- practical feasibility is more important than statistical representativeness
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[1] Source: Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited