Collecting survey feedback is a great way to find out how people feel about brands, products, services, public figures, social issues, and more—but getting a representative sample of your audience can be a challenge. After all, you can hardly poll the hundreds of millions of people living in the United States. Probability sampling ensures that you can get accurate results from a much smaller sampling.
Probability sampling uses a small, randomly selected group of people from a larger population, then predicts how likely it is that their responses will be representative of the larger population. Here’s an overview of probability sampling, types, and examples.
What is probability sampling?
Probability sampling uses a smaller sample group from a larger population to predict the responses of the larger population.. To perform an accurate probability sample, you’ll need to ensure that everyone in the population has an equal chance of being selected for the survey, and you must know what the chance of being selected may be for each person.
Probability sampling should be random—that is, if you have a population of 500, everyone should have a 1 in 500 chance of receiving the survey. Then you can choose a selection method, whether that’s assigning numbers to each person and using a random number generator, or pulling names out of a hat. The key is to make sure that no one is overrepresented, such as putting their name into the hat multiple times.
If you follow these guidelines, the sample you create is far more likely to be representative of the larger population.
Why use probability sampling?
The main advantage of probability sampling is that it allows you to get accurate feedback about a larger population, without having to poll the entire population. For example, think about political polling: it would be nearly impossible to poll every single registered voter in a single state, let alone all of America. Not only are there hundreds of millions of registered voters, but there’s no guarantee that they’d pick up the phone, open your email, or pay attention to that push notification.
Instead, pollsters use probability sampling. They identify a large frame, then create a representative sample of American voters. That way, they can poll fewer people—several thousand instead of millions—but still get an accurate reflection of voter sentiment.
Companies can do this, too. By using probability sampling in your surveys, you can ensure that your feedback reflects your consumer base as a whole, not just the people who had an issue with customer service or people who bought products five years ago. This leads to more accurate, actionable insights.
Probability sampling methods
There are four broad methods of probability sampling:
- Simple random sampling: This is the “pull a name out of a hat” method, in which all members of the larger population have an equal chance of being selected. The selection is done randomly. The drawback to this method is that it’s prone to bias. If your sample size is too small, relative to the larger frame, you’re less likely to pick reliable random samples.
- Interval sampling: This method assigns every member of the population a number, then selects individuals at regular intervals. For example, every tenth person becomes part of the sample. There are certain drawbacks to this method, too: it might not be as random as simple random sampling, and if there are any hidden patterns in the larger population list, it could skew your results.
- Stratified random sampling: This method divides the larger frame into specific groups that do not overlap, but when put together, they reflect the overall population. This could be groups like “have created a user account and made a purchase” vs. “have created a user account but have not made a purchase.” Common stratified characteristics include gender, age, ethnicity, and other mutually exclusive categories. Once you’ve stratified your population, you can use simple random sampling to select people from each group, proportional to the overall population.
- Cluster sampling: Cluster sampling separates the larger population into subgroups—but unliked stratified random sampling, the clusters are smaller versions of the overall population. Pollsters can randomly select entire clusters, or randomly select individuals from each cluster. Clusters might be sorted by organizations (universities, corporate offices) or geographic locations (states, cities, counties). The drawback to cluster sampling is that there’s no guarantee every cluster actually represents the overall population.
Probability sample examples
Imagine you wanted to poll a population of 100,000 people, but you only have the time and budget to collect responses from 1,000 of them. Probability sampling would help you create a representative sample from just 1 percent of the population. How you create that sample depends on who the population is.
For instance, if you were trying to get an accurate sample of a city, you might use stratified random sampling based on a mutually exclusive characteristic, like age groups. On the other hand, if you were polling employees of a company, you might choose cluster sampling, dividing the 100,000 frame into smaller clusters based on office location. Alternatively, you could use interval sampling—but it would be important to randomize the employee names to ensure that you don’t accidentally choose disproportionate numbers of senior employees and management, or people from specific offices.
While no probability sampling method is perfect, choosing the right type for your survey will ensure that you get a better representative sample of the overall population. Ultimately, this saves time and money, and gives you a clearer picture of what your target audience actually thinks.
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