What Is Probability Sampling?  Types & Examples
Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling.
To qualify as being random, each research unit (e.g., person, business, or organisation in your population) must have an equal chance of being selected. This is usually done through a random selection process, like a drawing, to minimise the risk of selection bias.
Types of probability sampling
There are four commonly used types of probability sampling designs:
Simple random sampling
Simple random sampling gathers a random selection from the entire population, where each unit has an equal chance of selection. This is the most common way to select a random sample.
To compile a list of the units in your research population, consider using a random number generator. There are several free ones available online, such as random.org, calculator.net, and randomnumbergenerator.org.
Stratified sampling
Stratified sampling collects a random selection of a sample from within certain strata, or subgroups within the population. Each subgroup is separated from the others on the basis of a common characteristic, such as gender, race, or religion. This way, you can ensure that all subgroups of a given population are adequately represented within your sample population.
For example, if you are dividing a student population by college majors, Engineering, Linguistics, and Physical Education students are three different strata within that population.
To split your population into different subgroups, first choose which characteristic you would like to divide them by. Then you can select your sample from each subgroup. You can do this in one of two ways:
 By selecting an equal number of units from each subgroup
 By selecting units from each subgroup equal to their proportion in the total population
Systematic sampling
Systematic sampling draws a random sample from the target population by selecting units at regular intervals starting from a random point. This method is useful in situations where records of your target population already exist, such as records of an agency’s clients, enrollment lists of university students, or a company’s employment records. Any of these can be used as a sampling frame.
To start your systematic sample, you first need to divide your sampling frame into a number of segments, called intervals. You calculate these by dividing your population size by the desired sample size.
Then, from the first interval, you select one unit using simple random sampling. The selection of the next units from other intervals depends upon the position of the unit selected in the first interval.
Let’s refer back to our example about the political views of the municipality of 4,000 inhabitants. You can also draw a sample of 100 people using systematic sampling. To do so, follow these steps:
 Determine your interval: 4,000 / 100 = 40. This means that you must select 1 inhabitant from every 40 in the record.
 Using simple random sampling (e.g. a random number generator), you select 1 inhabitant.
 Let’s say you select the 11th person on the list. In every subsequent interval, you need to select the 11th person in that interval, until you have a sample of 100 people.
Cluster sampling
Cluster sampling is the process of dividing the target population into groups, called clusters. A randomly selected subsection of these groups then forms your sample. Cluster sampling is an efficient approach when you want to study large, geographically dispersed populations. It usually involves existing groups that are similar to each other in some way (e.g., classes in a school).
There are two types of cluster sampling:
 Single (or onestage) cluster sampling, when you divide the entire population into clusters
 Multistage cluster sampling, when you divide the cluster further into more clusters, in order to narrow down the sample size
Multistage sampling is a more complex form of cluster sampling, in which smaller groups are successively selected from larger populations to form the sample population used in your study.
In stratified sampling, you divide your population in groups (strata) that share a common characteristic and then select some members from every group for your sample. In cluster sampling, you use preexisting groups to divide your population into clusters and then include all members from randomly selected clusters for your sample.
Examples of probability sampling methods
There are a few methods you can use to draw a random sample. Here are a few examples:
Fishbowl draw
You are investigating the use of a popular portable e‐reader device among library and information science students and its effects on individual reading practices. You write the names of 25 students on pieces of paper, put them in a jar, and then, without looking, randomly select three students to be interviewed for your research.
All students have equal chances of being selected and no other consideration (such as personal preference) can influence this selection. This method is suitable when your total population is small, so writing the names or numbers of each unit on a piece of paper is feasible.
Random number generator
Suppose you are researching what people think about road safety in a specific residential area. You make a list of all the suburbs and assign a number to each one of them. Then, using an online random number generator, you select four numbers, corresponding to four suburbs, and focus on them.
This works best when you already have a list with the total population and you can easily assign every individual a number.
RAND function in Microsoft Excel
If your data are in a spreadsheet, you can also use the random number function (RAND) in Microsoft Excel to select a random sample.
Suppose you have a list of 4,000 people and you need a sample of 300. By typing in the formula =RAND() and then pressing enter, you can have Excel assign a random number to each name on the list. For this to work, make sure there are no blank rows.
This video explains how to use the RAND function.
Probability vs. nonprobability sampling
Depending on the goals of your research study, there are two sampling methods you can use:
 Probability sampling: Sampling method that ensures that each unit in the study population has an equal chance of being selected
 Nonprobability sampling: Sampling method that uses a nonrandom sample from the population you want to research, based on specific criteria, such as convenience
Probability sampling
In quantitative research, it is important that your sample is representative of your target population. This allows you to make strong statistical inferences based on the collected data. Having a sufficiently large random probability sample is the best guarantee that the sample will be representative and the results are generalisable.
Nonprobability sampling
Nonprobability sampling designs are used in both quantitative and qualitative research when the number of units in the population is either unknown or impossible to individually identify. It is also used when you want to apply the results only to a certain subsection or organisation rather than the general public.
Advantages and disadvantages of probability sampling
It’s important to be aware of the advantages and disadvantages of probability sampling, as it will help you decide if this is the right sampling method for your research design.

Advantages of probability sampling
There are two main advantages to probability sampling.
 Samples selected with this method are representative of the population at large. Due to this, inferences drawn from such samples can be generalised to the total population you are studying.
 As some statistical tests, such as multiple linear regression, t test, or ANOVA, can only be applied to a sample size large enough to approximate the true distribution of the population, using probability sampling allows you to establish correlation or causeandeffect relationship between your variables.

Disadvantages of probability sampling
Choosing probability sampling as your sampling method comes with some challenges, too. These include the following:
 It may be difficult to access a list of the entire population, due to privacy concerns, or a full list may not exist. It can be expensive and timeconsuming to compile this yourself.
 Although probability sampling reduces the risk of sampling bias, it can still occur. When your selected sample is not inclusive enough, representation of the full population is skewed.
Frequently asked questions about probability sampling
 What is a sampling method?

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method.
This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling, convenience sampling, and snowball sampling.
 What is the difference between stratified and cluster sampling?

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous, so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous, as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population.
 What is a sampling frame?

A sampling frame is a list of every member in the entire population. It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.
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