A Guide to Probability Sampling (With Definition and Types)

By Indeed Editorial Team

Published May 27, 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.

Sampling techniques play an important role in statistics because they allow statisticians to make inferences about populations using only a small data sample. There are various types of sampling techniques, but the most common is probability sampling. This sampling technique allows researchers to study a population by selecting a sample representative of that population.

In this article, we discuss what this type of sampling is, highlight some different types, provide its advantages, and explain how to use it in practice.

What is probability sampling?

Probability sampling is a research technique that helps researchers select a sample that represents a population. This technique uses random selection methods to choose and survey a subgroup, reducing bias and generalizing findings to a larger population.

Researchers may use this technique to generate statistics that explain a larger trend. It also helps to identify and correct any imbalances in the population. Since the sample group is usually a subset of a population, researchers can often generalize the sample group's responses to the population.

Related: What Is Quantitative Analysis?

Types of probability-based sampling

The most common types of this sampling method include the following:

Simple random sampling

Simple random sampling is a sampling technique that uses a table of random numbers or an electronic random number generator to select samples. This method can be useful for large populations. For example, a researcher may use a random number table to choose a sample of 100 individuals from a population of 10,000 people. They may also use a lottery system or computer software to conduct the automated selection process.

Random cluster sampling

Random cluster sampling is a sampling technique that selects clusters (i.e., groups of individuals) at random and then selects a sample from each cluster. This method is often suitable for an unevenly dispersed population. Random cluster sampling allows researchers to study a population subset with minimal travelling because they can conduct in-person sampling research within a few clusters. Because a population's geographic area may have hundreds or thousands of clusters, it's often easier to travel to only three or four of them.

Stratified random sampling

Stratified random sampling is a sampling technique that divides the population into strata (i.e., groups of individuals with shared characteristics) and then randomly selects a sample from each stratum. This method is often suitable for heterogeneous populations, which means it has various properties. By stratifying the population, researchers can ensure that the members of each subgroup have equal opportunities to participate in these studies.

Systematic sampling

Systematic sampling is a sampling technique that uses a numerical interval system to select samples from a population. For example, researchers may assign a number to every member of a population and choose individuals at regular intervals, such as selecting every fifth person in the sample. This randomization can result in a subgroup that reflects the general population's variance.

Multistage sampling

Multistage sampling is a random sampling method that selects a sample in multiple stages, and each stage can introduce its sampling method. For example, researchers may divide a population's region into clusters, randomly selected using random cluster sampling. Then they may sample those random clusters using stratified random sampling, simple random sampling, systematic sampling, or another round of random cluster sampling. The researchers can perform additional rounds of sampling until they settle on a final subgroup to study.

Multi-phase sampling

This sampling method differs from multistage sampling because it doesn't involve selecting clusters or dividing a population into subgroups. In multi-phase sampling, researchers randomly select a sample and divide it into subgroups. The researchers may use any of the probability-based sampling techniques to select the subgroups.

Probability proportion to size sampling

Probability proportion to size sampling is a sampling technique that selects samples in proportion to their sizes in the population. This method ensures that equal opportunities exist for all members during the selection process. Researchers often use this technique to study all the members in a population.

Nonprobability sampling

Nonprobability sampling, also known as nonrandom sampling, is a sampling technique that relies on chance, judgment, or some other method to select participants. The primary advantage of nonprobability sampling is that it's often less expensive and time-consuming than probability-based sampling. The disadvantage of using this technique is that it may be less reliable because the selection process isn't random. There are several types, including:

  • Convenience sampling: Researchers may use this technique to conduct a quick survey on people who are easy to find and poll. This might include individuals who live in a neighbourhood or people attending a sporting event.

  • Quota sampling: This technique involves selecting a specific number of participants from each subgroup in the population. For example, researchers may use quota sampling in a study sample that includes men and women to select them equally.

  • Purposive sampling: Researchers may use purposive sampling to target a specific population, such as people actively looking for a new job. This technique involves selecting participants who meet certain predefined criteria.

  • Snowball sampling: Snowball sampling is a purposive sampling technique that uses referrals to identify potential participants. For example, researchers may ask participants to refer their friends and family to take a survey and quickly generate a large sample size.

Related: Guide to Standard Deviation in Finance and How to Calculate

Advantages of probability-based sampling techniques

The following are some advantages of using probability-based sampling techniques:

  • Reduces bias: This survey technique allows researchers to create a sample that represents a population. This means that a survey's results are typically more accurate than nonprobability sampling methods.

  • Improves precision: With this sampling method, researchers can estimate the accuracy of their results by calculating the standard error. This statistic measures how much variability there is in a study's results. Small standard errors typically ensure the result's precision.

  • Saves money: Researchers can use the sample to identify and target specific populations for further study. This can subsequently save time and money and improve the result's accuracy.

  • Provides simplicity: This sampling method can provide straightforward techniques that are easy to use. This ensures that researchers can use the information from the study accurately and effectively.

  • Saves time: This technique can help researchers collect data more quickly and efficiently than nonprobability sampling methods. This ensures that the researchers can analyze and interpret the data.

Related: 12 Jobs for Statistics Majors (With Salaries and Duties)

How to conduct probability-based sampling methods

Here are the steps that you can use to conduct probability-based sampling:

1. Choose your population

Begin the sampling process by choosing a population that you want to study. The population refers to the entire group you seek to understand and analyze. You can target any population of interest, such as the employees of a large company, the students of an academic institution, or the registered voters of a city or state. It's important to note that the population you choose may not be finite, and you can study a constantly changing population, such as the customers of a retail store.

2. Establish your sampling frame

A sampling frame, also known as a sample frame or survey frame, lists all the elements in the population you want to study. Researchers can create this list in several ways, such as using information from government records or population surveys. Once you have your sampling frame, you can use it to randomly select participants for your study.

3. Select your probability sample

After choosing your population and establishing your sample frame, you can randomly select your probability sample. When selecting the subgroup, you can use simple random sampling, random cluster sampling, stratified random sampling, systematic sampling, or multistage sampling techniques. It may be useful to assess the population to determine which sampling method may work best.

4. Analyze your results

Once your subgroup completes their surveys or interviews, you can analyze the data. This process includes examining the distribution of responses, calculating statistics, and drawing conclusions about the population. You may survey a subgroup of employees at a large company regarding their benefits package and find that they're happy with their benefits but may prefer more paid time off. This data may be helpful to the human resources team as they update the company's benefits packages for the upcoming fiscal year.

What to avoid when creating a sampling frame

While this sampling is typically a good method for collecting data, there are a few things to avoid when creating your sampling frame. Here are a few tips:

  • Biased frames: It's important to make sure that you have an unbiased sampling frame, which means that you provide all the elements in the population with an equal opportunity to participate in the study. You can do this by randomly selecting participants from your frame or using stratified random sampling.

  • Outdated information: Ensure that your sampling frame is up to date and includes the most recent information about your population. You can do this by surveying the population periodically or using administrative records.

  • Incomplete frames: Make sure that your sampling frame includes all the elements in your population. You can do this by verifying the information against other records or surveys.

  • Illegal frames: Be aware of the laws governing the population you're studying and ensure that your sampling frame doesn't violate any of these laws. You can do this by consulting with an expert in the field or the population's governing body.

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