The complete guide to representative samples

Andrew Gordon
|May 8, 2024

When collecting data through surveys or experiments, you need responses from the right people to draw reliable conclusions. But you can't include every single person in your target population – that would take far too much time and money.

For many, the solution is to use a representative sample. Only you might be wondering what it is and how it works. That's the purpose of this guide, which has everything you need to know about representative samples.

What is a representative sample?

Imagine youʼre a cereal company that wants to test out a new granola flavour. You could ask every single person in the country what they think, but that would take ages and cost a fortune. Instead, you could select a representative sample.

A representative sample is a small group of people that accurately reflects the larger population you want to study. Think of it like a mini version of your target audience. The sample includes people with diverse demographics, behaviors, and attitudes in the same proportions as the full population.

For the cereal company, a representative sample might include a mix of:

  • Men and women
  • Different age groups
  • Various regions of the country
  • Frequent and occasional cereal eaters
  • Health-conscious and indulgent snackers

Carefully selecting a representative sample allows you to collect reliable data that provides a true picture of how your target population thinks and acts. The insights you gain from a small group can be generalized to confidently draw conclusions about the wider audience.

The goal is to make sure your sample accurately mirrors the population. If your sample is skewed or biased, you end up with misleading results.

Stratified sampling

It’s one thing knowing what a representative sample is; it’s another knowing the nuances involved.

Getting a representative sample involves stratified sampling. This method is a bit like sorting your participants into different buckets based on key characteristics. Then you select a random sample from each of those buckets. Those characteristics – known as strata – could be things like age, gender, income level or geographic region.

With this method, you can make sure your representative sample includes the right mix of participants from each subgroup. It’s ideal when you want to compare results between different segments or need to oversample a rare population.

Why are representative samples important, and when should you use them?

Let’s say you’re trying to predict the outcome of an election by only polling a handful of your friends. Sure, you might get a sense of how your social circle is voting, but what about the rest of the country? Thatʼs where representative samples come in.

Representative samples are central for collecting reliable, unbiased data that reflects your entire target population.  By including a diverse mix of participants in the right proportions, you increase the chances of confidently drawing conclusions and making decisions based on your research results.

Here are a few scenarios where using a representative sample is especially important:

Generalizing findings

If you want your research insights to apply to a broad target population, a representative sample is necessary. It shows that your results accurately reflect the views and behaviors of the larger group.

Comparing subgroups

Planning to analyze differences between segments, like age groups or regions? Use a representative sample with enough participants from each subgroup to make meaningful comparisons.

Testing new products

Launching a new product or feature? A representative sample can help you gauge appeal and spot potential issues across your target market. No more basing decisions on a vocal minority.

Of course, there are some cases where you might not need a perfectly representative sample. If you're piloting a niche product, for instance, you may want to focus on a specific subset of your target audience.

The goal is to align your sampling approach with your research needs. When in doubt, remember that representative samples reduce bias in your data and help to keep your insights relevant to the people who matter most.

Examples of representative samples

We've covered the ins and outs of representative samples. Now let's see them in action with a few real-world examples.

Election polling

Picture this: itʼs the eve of a big election, and news outlets are buzzing with predictions about who will win. But how do they make these forecasts? The secret – or not so secret, depending on who you ask – is representative sampling.

Polling organizations use techniques like stratified sampling to survey a representative cross-section of likely voters. They ensure their sample has the right mix of demographics, political affiliations, and geographic areas to mirror the electorate.

By capturing a microcosm of the voting population, pollsters can extrapolate their results to project election outcomes. Thatʼs how they can make surprisingly accurate (most of the time) predictions based on a sample of just a few thousand people.

Consumer product testing

Letʼs say a cereal brand is dreaming up new cereal varieties. Before they hit the shelves, they need to know which ones will resonate with customers. Time to call in a representative sample.

The brand might use stratified sampling to select a representative subset of cereal lovers across different regions, age groups, and snacking habits. It allows them to conduct taste tests and surveys to gauge reactions to the new varieties.

This feedback means the brand can make data-driven decisions about which products to launch. They can feel confident that the winning varieties will appeal to their target market, not just a handful of people in the focus group room.

Public opinion research

Imagine the government is considering a new policy. To understand how the public feels about a hot-button issue, they turn to representative sampling. Researchers might use stratified sampling to select a representative subset of the adult population. Then they can conduct surveys or interviews to gauge support, concerns, and potential impacts of the policy.

By tapping into a representative sample, policymakers get a pulse on public opinion without surveying every single citizen. They can use these insights to shape the policy and communication strategies that strike the right chord with the people they serve.

From snacks to politics, representative samples help researchers make decisions with confidence. With the right snapshot of the target population, they provide the data needed to drive real-world impact.

Representative samples versus other sampling methods

While representative sampling is a powerful tool for collecting less biased data, itʼs not the only game in town. Researchers have a variety of sampling methods at their disposal, each with its own strengths and use cases.

So, how does representative sampling stack up against other popular approaches? Letʼs take a closer look at some key alternatives and when you might choose them over a representative sample.

Convenience sampling

Convenience sampling is like grabbing the first people you see at the grocery store. It's a quick and easy way to collect data from readily available participants, like your social media followers or opt-in survey panels.

The downside? Convenience samples are rarely representative of the broader population. They tend to be skewed toward certain demographics or behaviors, which can lead to biased results.

Use convenience sampling when you need to gather data fast and donʼt require a representative sample, like for a pilot study or low-stakes poll. But for more rigorous research, you'll want to level up to a representative approach.

Snowball sampling

Snowball sampling is similar to playing a giant game of referral tag. Researchers start with an initial group of participants before asking them to recruit more people from their networks. The sample grows organically, like a snowball rolling down a hill.

It’s a method useful for studying hard-to-reach or hidden populations, like marginalized communities or rare disease patients. Researchers can connect with people they might not find through other channels by tapping into participantsʼ social networks.

The catch? Snowball samples are prone to bias since participants tend to refer others who are similar to them. The final sample may not be representative of the broader population of interest.

Consider snowball sampling when your target audience is tough to access through conventional methods.


At the end of the day, the best sampling method depends on your research question, resources and goals. What really matters is understanding the strengths and limitations of each approach. That way, you can choose the right tool for the job and collect the high-quality data you need to drive meaningful insights.

How to collect a representative sample

What’s the best way to go about collecting a representative sample? With the right approach, you can assemble a balanced, unbiased group of participants that mirrors your target population.

Define your target population

First things first: who are you trying to study? Before you can select a representative sample, you need a clear picture of your target population.

Start by thinking about the key characteristics that define your population of interest. What demographics, behaviors, or attitudes are most relevant to your research question? Make a list of these criteria, as theyʼll be your guide as you build your sample.

Keep in mind that your target population should align with your research goals. If you're studying attitudes toward a new product, for instance, your population might be "adults aged 18-65 who regularly purchase similar products." The more specific you can be, the better.

Choose a sampling frame

Once you know who you're studying, itʼs time to find them. A sampling frame is a list or database that includes a representative set of members of your target population. Itʼs the pool from which you'll draw your representative sample.

  • Depending on your research context, your sampling frame might be a phone directory, customer database or online panel. The key is making sure your frame covers your target population as completely and accurately as possible.
  • Studying a broad group, like all adults in the UK? You may need to combine multiple frames or tap into a large, nationally representative panel to capture the full picture.
  • Zeroing in on a niche population? Time to get creative. You might need to explore specialized databases or communities to find your target audience.

Stratify your sample

Use stratified sampling to ensure representation from key subgroups, like age or income brackets. Divide your frame into strata, then randomly sample from each one.

Determine your sample size

Last but not least, you'll need to decide how many people to include in your representative sample. The right sample size depends on a few factors, such as the:

  • Level of precision you need in your results
  • How many factors you’re stratifying by to make them representative

As a rule of thumb, larger samples tend to provide more precise estimates. But they also require more time and resources to collect and analyze.

To strike the right balance, many researchers use power analysis or online sample size calculators. These tools help you determine the minimum sample size needed to detect meaningful effects or differences in your data.

For example, if you're comparing customer satisfaction across two brands, you'll need a sample size large enough to pick up on any significant gaps. A power analysis can tell you exactly how many responses you'll need from each brand's customers to draw confident conclusions.

With these steps and tools like Prolific's diverse participant pool, you can collect unbiased, reliable data that speaks volumes about your target population.

Summary: Representative sampling

Representative sampling is a powerful aid for gaining reliable insights about your target population. Careful selection of a balanced, unbiased sample that mirrors the broader group will help you make confident decisions based on your research results. With a representative sample in hand, you're well on your way to driving meaningful impact with your research.

Ready to jump into representative sampling for your research? Sign up for Prolific today and tap into our diverse, high-quality participant pool to power your projects with reliable, unbiased data.