## What is Probability Sampling?

Probability sampling is the process of selecting a sample using random sampling. When you use this method, each individual or unit in a population has a known, non-zero chance of being randomly selected for the sample. Statisticians consider this method the most reliable because it tends to minimize sampling bias and produce samples that accurately represent the entire population. A representative sample allows you to use the sample to make inferences about the population.

In this blog post, I discuss the various types of probability sampling and highlight its benefits over non-probability sampling methods.

Learn more about Types of Sampling Methods in Research.

## Types of Probability Sampling

There are several types of probability sampling methods. Each method has a unique way of selecting a sample from a population. Researchers can choose from various techniques depending on the characteristics of the population and their research goals.

### Simple Random

Simple random sampling is the fundamental form of probability sampling, where each member of a population has an equal likelihood of being chosen for a sample. For example, if a teacher wants to know the average age of her students, she could use a simple random sample by numbering each student and using a random number generator to select the sample. Or she can randomly draw their numbers from a hopper.

Learn more about Simple Random Sampling.

### Systematic

Systematic sampling involves selecting every n^{th} individual from a population to create a sample. It is a simpler and more convenient probability sampling method than simple random sampling. Researchers can use this method for large populations. It is less time-consuming than other methods while maintaining the randomness of the selection process.

For example, if a researcher wants to survey students about their study habits, they could select every 10^{th} student from the class list to create their sample.

Learn more about Systematic Sampling.

### Stratified

Stratified sampling involves dividing a population into subgroups or strata based on common characteristics, such as age or income level. Researchers then use probability sampling to draw a sample from each stratum randomly.

This method ensures that the sample accurately represents each subgroup in the population, which can increase the precision and accuracy of estimates. It also facilitates comparisons between subgroups.

For example, if a company wants to survey its customers about their products, it could divide its customers into age groups and then randomly select a sample from each group.

Learn more about Stratified Sampling.

### Cluster

Clustering is a probability sampling method that divides a population into similar clusters or groups. Then researchers randomly select a sample of clusters to survey. This method is beneficial for geographically dispersed populations because it can reduce the amount of travel.

For example, if a researcher wants to know a city’s crime rate, they can randomly select a few neighborhoods and survey every household in them.

Learn more about Cluster Sampling.

## Probability vs Non-probability Sampling

In probability sampling, each population member has a known, non-zero probability of being randomly selected.

Conversely, non-probability sampling occurs when the researchers do not randomly select the participants. Instead, these methods rely on the researcher’s expertise, judgment, or convenience in choosing them.

Probability sampling has several advantages including being:

- More likely to produce unbiased, representative samples, allowing you to use the sample to make inferences about the population.
- More reliable for conducting statistical hypothesis testing because it allows these procedures to calculate the standard error accurately.

Conversely, non-probability sampling methods are cheaper and easier to implement. Unfortunately, they are more likely to produce biased samples that do not accurately represent the population. These problems place significant constraints on the generalizability of the results.

Learn more about the following non-probability methods:

When conducting a research study, it is crucial to consider the type of sampling you will use. Probability sampling is the most reliable type, but it may not always be feasible or cost-effective. If you cannot use it, carefully consider the limitations of other approaches.

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