What is non-probability sampling? (Uses, types and benefits)

By Indeed Editorial Team

Published 15 June 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you're considering starting a career in data science, you may benefit from understanding the practical uses of non-probability data. You can then gather data more easily without biasing your sample, increasing the accuracy of the results. This guide shows how to apply this concept in a professional context. We define the term 'non-probability sampling', explain when you can use it, outline six types of this method and detail its core benefits.

What is non-probability sampling?

Non-probability sampling is a data selection method that permits researchers to choose data samples based on their own judgement. As it relies on the person's experience and analytical skills, this sampling process is often more vulnerable to discrepancies than probability-based methods. Here researchers can decide which individuals may participate in a project, which could generate inaccurate trends if they're unrepresentative of either the more comprehensive sample or society generally.

If you're designing a statistical study, you can take several steps to reduce sample bias, and thus ensure you make informed decisions about which subjects to use. For example, you could build a test profile of your ideal subjects, detailing their common identifying features, such as their age, profession or gender. You can then exclude individuals who fail to meet these criteria. Conversely, you may outline your investigation's action plan before creating the sample, detailing its hypothesis, methodology and data analysis stages. You can then use this information to filter participants based on their relevance to the study.

Related: What is a biased sample? (Examples and tips to avoid it)

What are the purposes of this sampling method?

The section below outlines three practical uses of this sampling method:

Creating opinion polls

You could use this sampling method to create opinion polls to learn about voters' political views if you're a political analyst. In this context, you can design polls to focus on either respondents' voting intentions or their position on specific issues, such as defence spending or climate change. To gather data, you could develop an online survey, asking respondents to discuss both the topic and useful defining features, such as their age or household income. You can then compile this data to uncover how people's political leanings may vary depending on their background.

Gathering consumer data

If you work in sales, you could use this sampling method to create market surveys to learn if consumers are satisfied with your current product range. In this situation, you can draft a series of introductory questions to uncover each person's age, gender and wealth, only letting those who fit your target market proceed further. In the primary survey, you can ask open-ended questions that cover the product's core specifications, such as if it's simple to use or offers value for money. You may then use this data to influence product development in the future, protecting your firm's competitiveness.

Related: Guide to creating customer satisfaction survey questions

Increasing consumer awareness

You could also use this sampling method to spread awareness of your firm's products amongst members of its target audience. In this situation, you may construct a profile outlining these individuals' typical attributes, such as their values, media habits or interests. You could then use targeted digital adverts to inform them of the survey's existence before detailing personalised rewards that they could earn by completing it. By taking this approach, you're more likely to convince them to buy the product, increasing your firm's revenue as a result.

Related: What is customer profiling and how do you create one?

Types of sampling

The next section details six types of this sampling method, detailing the basic process behind each method:

Convenience sampling

Convenience sampling involves gathering data samples from people who are located nearby, or easily available, making it easier to collect a large sample quickly. If you're a researcher, this method can be used to learn about the public's opinion of a topic without creating a lengthy recruitment process. You can find this helpful if you require new consumer data immediately to direct short-term campaigns. For example, if you're a sales executive for a cinema, you can use this method to learn whether visitors enjoyed a recent film release. You may then opt to either expand or reduce the number of screenings.

As you can't determine the group's members in advance, it's possible that it's unrepresentative of the overall population. This may limit the utility of your data sample by identifying unreliable trends, causing you to take harmful business decisions. Before using this method, it's advisable to decide if you prioritise convenience or reliability, taking into account external pressures, such as the time available to gather data.

Related: How to calculate probability (with real-life applications)

Consecutive sampling

Consecutive sampling involves selecting a random individual or group from a data set, analysing their results and identifying useful trends. Another individual or group is then selected from the data set, repeating the previous process. It's possible that these individuals don't represent the wider population, though you can rectify this issue by using a larger sample size. As you're compiling participants' data in order, you can gather it in public spaces, such as a shopping centre or cinema, rather than using digital surveys.

Voluntary response sampling

Voluntary response sampling involves gathering data on a topic from a set of individuals who've volunteered to participate in your study. As they've chosen to participate in this study without prompting, it's likely that these individuals hold strong opinions about the topic in question. In this scenario, you could gather a biased sample, as moderate individuals are either unaware of the study or don't want to discuss polarising subjects. To minimise the risk of sample bias, you may create a minimum sample size threshold for the study to meet before you publish its results.

Related: What is Bayes' formula and what do you use it for?

Purposive sampling

Purposive sampling involves selecting individuals you believe are well-suited to participate in your study. Depending on a study's focus, you can use various criteria to filter candidates, such as soft skills or profession, though such factors aren't rigid guidelines. If you use this method, it's useful to possess extensive knowledge of the topic in question to ensure that you can precisely judge which individuals can contribute reliable data. If not, irrelevant outliers could skew your data set by highlighting unrelated trends.

Snowball sampling

Snowball sampling involves using a study's existing participants to recruit new ones, as boosting the sample size gives its results more credibility. Depending on the nature of this study, this method may either boost or reduce the usefulness of your results. For example, if you're studying doctors' opinions of a recent health bill, you could ask participants to contact their colleagues, allowing you to incorporate more expert opinions into your data set. Conversely, if you're sourcing data about citizens' view of a divisive political topic, respondents are more likely to contact people who agree with them, skewing your results.

Quota sampling

Quota sampling involves creating a sample including a pre-determined number of individuals representative of the wider population. In this scenario, you can attribute certain values or traits to the sample, such as their political affiliation, gender or age. You can then source various participants who fit this profile, asking them a series of questions about the topic in question. Depending on your investigation's scope, you can divide this quota into several sub-quotas, each with more specific traits. You could then compare results from each sub-quota to identify similarities and differences between their responses.

Related: Inferential statistics: definition, tips and applications

Benefits of using this sampling method

The section below outlines two benefits of using this sampling method:

Reduce research times

One benefit of using this sampling method is that you could gather large pools of data in a short time period. As you can choose who to sample based on whether they possess certain desirable traits, you may eliminate irrelevant actors without having to contact them. You might also tailor research questions to include content that appeals to viable candidates, increasing the likelihood that they're willing to answer them. You may then gather data without being rejected by potential respondents, boosting your own productivity as a result. This kind of sampling is thus usually relatively inexpensive.

Learn more about specific groups

A second benefit of using this sampling method is that you can learn about specific demographic groups in greater detail. In this context, you can use purposive questioning to prioritise individuals who possess certain traits or knowledge before asking for their opinion about a set topic or problem. You may then use this data set as a cross-section of that group's viewpoint before designing solutions that best suit these specifications.

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