Probability sampling is a sampling technique in which respondents are chosen through random selection. This means that any member of the population has an equal chance of being selected. Probability sampling methods are largely thought to provide the most representative sample of an overall population.

Examples of probability sampling include simple random sampling, systematic sampling, cluster sampling, and stratified sampling. When used in scientific studies and survey research, these sampling approaches can use a relatively small sample size to make accurate inferences about an entire population.

Use probability sampling to collect data with the lowest risk of sampling error. Random selection allows study samples to form without the influence of researcher bias—whether that bias owes to convenience or misguided hypotheses. Probability sampling is typical in scientific studies, market research, survey sampling, and opinion polling.

Probability sampling methods can be characterized by the following traits.

1. Probability sampling uses random sampling techniques to amass respondents. People can be chosen for probability sampling through the use of a random number generator or a systematic method. In either case, all members of the population have an equal chance of being selected for a study.

2. Probability sampling is the opposite is non-probability sampling. Non-probability sampling does not study individuals based on random chance. Examples include quota sampling (which targets a population of interest), convenience sampling (where surveyors choose respondents based on the ease of recruiting them), and snowball sampling (where surveyors ask existing study participants to help them find additional subjects).

3. Probability sampling designs can include multistage sampling. Although more time-consuming, some probability sampling techniques involve sampling a target population and then creating subgroups or subpopulations for further testing. The probability sampling strategy dictates that whenever study respondents get broken into smaller groups, this is done via random selection.

Four main types of sampling fall under the banner of probability sampling. All four use random selection as a key element.

1. Simple random sampling: In simple random sampling, individuals are chosen from a whole population at random. These individuals could be assigned numbers and then a random number generator selects from among these numbers. This is effectively how telephone surveying works.

2. Systematic random sampling: Systematic sampling builds upon random selection by using a more defined method for picking participants. For instance, people walking into a room could be asked to pick a numbered slip of paper out of a hat so that everyone gets a number assigned to them. Researchers could then announce that everyone whose number ends in five has been chosen for a study. This is a form of probability sampling because the numbers were assigned to people at random.

3. Cluster sampling: Cluster sampling begins by dividing a total population into clusters that have similar characteristics. Researchers then pick a small number of randomly selected clusters to study further. For instance, if researchers wanted to study elementary schoolers in a district, they could make each individual school a cluster and pick three of those schools to study. They can also enact multistage sampling where individual students from those schools get randomly selected for further analysis.

4. Stratified sampling: This is a form of probability-based multistage sampling. The starting point of stratified sampling is separating a sampling frame into subsets called strata. Each stratum will be intentionally homogenous for a specific characteristic. For instance, at an international conference, this stratification could be based on nationality with all the Americans in one stratum, all the Canadians in a different stratum, and so forth. Then, within each stratum, respondents are chosen at random for individual study. Researchers use this type of sampling when weighting for population imbalance.

The overwhelming advantage to probability sampling is the accuracy it provides. Compared to a sample chosen based on a researcher’s instincts, choosing a group of people at random provides a sample that is more likely to be representative of the population. It avoids researcher bias and, when properly structured, prevents the over-representation or under-representation of certain factions of society.

The prime disadvantage of probability sampling is that it can be expensive and time-consuming to do right. Startup companies and individuals may not be able to afford the costs and wait times that come with hiring professional researchers who use probability sampling. Such people may need to settle for convenience sampling or quota sampling, which can be cheaper to pull off but have a history of producing less accurate data. Probability sampling can also be overkill for some market researchers who do not actually need a full portrait of the population at large. For these people, quota sampling—with its laser focus on populations of interest—may be more appropriate.