# Probability vs. Non-Probability Sampling: Key Differences

By Indeed Editorial Team

Published 9 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.

Understanding different research sampling methods can help you decide which to use for your project. In this article, we look at the difference between probability vs non-probability sampling and explain how they're used.

Related: Research Skills: Definition, Examples and Importance

## What is probability vs non-probability sampling?

Probability sampling is the process of selecting respondents at random to take part in a research study or survey. Each person in a given population has an equal chance of being selected. Researchers use this type of sampling when conducting research on public opinion studies.

Non-probability sampling, on the other hand, is a non-random process of selecting research participants. This method is used when researchers have a particular target group in mind. For example, a company developing accounting-related software is likely to conduct non-probability sampling to select only accountants as part of their research sample.

Related: What Is Market Research? Definition, Types and Examples

## How to conduct probability sampling

There are four ways to conduct probability sampling:

### 1. Simple random sample

To perform a simple random sample, each member of the research pool/population is given an identifier (usually a number). Next, identifiers are selected at random by an automated software programme.

### 2. Stratified random sample

To perform a stratified random sample, the research pool/population is first divided into sub-groups (such as male and female). A random sample is then performed on each sub-group.

### 3. Cluster sample

In cluster sampling, the research pool is divided into clusters. The researcher then chooses a select number of participants randomly from each cluster.

### 4. Systematic sampling

Systemic sampling involves the selection of participants from the research pool at fixed intervals. For example, a researcher may decide to create a sample from every 5th person in the pool. The sample would therefore consist of the 5th, 10th, 15th, 20th person and so on.

Related: What Does a Market Research Analyst Do? Plus Salary

## Methods of non-probability sampling

Non-probability sampling can also be done in 4 ways:

### 1. Convenience sampling

A convenient sample consists of participants who were conveniently accessible to the researcher. Convenience sampling is fast and simple, but may not produce results that are applicable to the entire population.

### 2. Snowball sample

Snowball samples are created by participants recruiting other participants. A researcher who is conducting a survey of football fans, for example, can recruit a set number of fans who then are asked to recruit other fans.

### 3. Quota sample

To conduct quota sampling, the research population is first divided into subgroups based on certain characteristics (such as age or location). Researchers then select participants from each sub-group in a non-random fashion.

### 4. Purposive or judgmental sample

A purposive or judgmental sample is made up of participants who were all intentionally selected by the researcher. They consist only of people who fit particular niche criteria.

Related: What Does a Research Analyst Do? (With Skills and Salary)

## Advantages and disadvantages of probability sampling methods

Each method of sampling has its own advantages and disadvantages. Some of these include:

Simple random sample: Simple random samples are time-consuming to create, but are highly representative of the general population.

Stratified random sampling: Similarly, stratified random sampling is also representative of the population as a whole but is difficult to perform.

## Advantages and disadvantages of non-probability sampling methods

There are also advantages and disadvantages to using non-probability methods. Some of these include:

Judgmental sampling: Judgmental sampling, whilst easy to perform, may lead to biased results.

Snowball sampling: Recruitment in snowball sampling is usually quick, but may produce false results as the recruitment process is out of the hands of the researcher.

Related: 10 Most Essential Data Analysis Skills

## Uses of probability vs non-probability sampling

Probability sampling usually produces results that are more representative of the entire population than non-probability sampling. Non-probability sampling, however, is more appropriate for research studies that involve only people of a certain demographic/criteria.

This article is based on information available at the time of writing, which may change at any time. Indeed does not guarantee that this information is always up-to-date. Please seek out a local resource for the latest on this topic.

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