What is a representative sample, and how do you get one?

In business, healthcare, and the social sciences, the aim of research is often the same: understanding a larger group based on a smaller part of it. This smaller part is called a sample. However, not all samples actually give accurate results. A sample that really represents the larger population is called a representative sample. 

Why is it important to have a sample that accurately reflects the entire population?

A valid representative sample will enable market researchers, scientific researchers, and data analysts to draw correct conclusions, predict trends, and make wiser decisions. Without it, research can be misleading, a waste of time, and a waste of investment.

In this article, we'll explain what a representative sample is, why it matters, how to get one, and give you real-life examples. Finally, you'll receive practical recommendations for using representative samples in surveys, data analysis, and research projects.

What is a representative sample?

A representative sample is a small group drawn from a larger population that accurately reflects the characteristics of the entire population. A simple example: if a population is a whole cake, then a representative sample is a slice that contains all the layers and ingredients. The representative sample looks like a "mini" version of the cake.

What's a representative sample, explained in numbers?

Imagine a city that's 60% women and 40% men. A sample of that city's residents should be roughly the same – if the sample were 1,000 people, 600 would be women and 400 men. Similarly, if 10% of the people in that city are over 65 years old, about 10% of your sample should be too.

As you understood from the representative sample examples above, the central idea is that this sample enables researchers to study patterns, behaviors, or attitudes in a small group while, at the same time, being confident that the findings apply more generally to the larger population.

Why it's important to get a representative sample

Getting a representative sample is necessary because that's how you ensure your research results are both valid and meaningful. Some core benefits of using representative samples include:

• Accurate results: If the sample is indeed representative of the population, then the conclusions you arrive at are most likely to be correct. In market research, you'll know what different groups like about products. In healthcare surveys, a representative sample can guide decisions about patient care strategies, reflecting the needs of the entire patient population, and so on.
• Reduced bias: A biased sample is a false picture. For example, a survey of only young people about pension plans will not yield results because it does not reflect the needs of the entire population.
• Resource savings: Surveying the whole population is usually impossible. A well-chosen sample yields reliable data efficiently.
• Support for decision-making: Businesses, policymakers, and researchers use data to make decisions. A representative sample will give them a basis for making better decisions.
• Boosted credibility: Representative samples are more reliable for colleagues, stakeholders, and the general public when research is based on them.

A representative sample helps to ensure that your research findings are reliable, actionable, and unbiased.

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How to get a representative sample

Start by defining population, defining a sampling method, and being careful to avoid common mistakes.

1. Define your population

Clearly define the population you intend to studied. This can be all customers of a company, students in a university, or patients who wait for treatment in a healthcare center. The population should be well-defined and relevant to your research goals.

2. Select a sampling method 

There are several ways of selecting a sample itself. Some common methods include:

Simple random sampling

Every member of the population you want to study has an equal chance of being selected.

Stratified sampling

The population is divided into groups (strata) such as age, income, or location, from which samples are drawn at random, proportionally.

Systematic sampling

Every nth person (meaning every 5th, 10th, etc.) is selected from a list. This is easy but may introduce bias if there are periodic patterns in the list. 

Imagine we have a list of students rotating between departments A, B, and C. If we select every third person, we'll select students numbered 3, 6, 9, and so on. In this case, all the selected students will be from department C, and students from departments A and B will not be included in the sample.

Cluster sampling

This method involves dividing the population into similar clusters, such as regions or schools, and then randomly choosing entire clusters.

3. Determine sample size

Representative sample size impacts the accuracy and the statistical significance of your findings. Larger samples are generally more representative, but they are also more expensive. With statistical formulas, you can find out the right sample size.

For a simple random sample, the formula is:

n = (Z² × p × (1 - p)) / E²

Where:

• n = required sample size
• Z = Z-value corresponding to your desired confidence level (e.g., 1.96 for 95%)
• p = estimated proportion of the population with the characteristic (use 0.5 if unknown)
• E = margin of error you can accept (e.g., 0.05 for 5%)

If the population is small, you can adjust or change the sample size by applying the finite population correction:

n_adjusted = n / (1 + (n - 1) / N)

Where:

• N = total population size
• n_adjusted = corrected representative sample size

4. Check for representativeness

Compare your sample's characteristics with those of the population. If certain groups are underrepresented, adjust your sample or weight the data to reflect population proportions.

A table like the one below shows discrepancies between the sample and the population.

5. Avoid common pitfalls

Watch out for common pitfalls that researchers and marketers often encounter:

• Selection bias – Make sure that your sample is not biased toward any particular group.
• Nonresponse bias – This involves encouraging participation from all selected members to avoid skewness of results.
• Overgeneralization – Only make conclusions about the population your sample represents. This helps ensure your sample truly represents the population and produces reliable results.

Examples of representative samples in research

Representative samples are applied in different areas of research. The areas range from market studies and customer analyses to healthcare administrative and doctor surveys, and political polls. Let's consider some examples:

In all cases, proper selection of the sampling method is important, as this ensures that the small group studied accurately reflects the wider population and ensures a reduction of bias. It also allows the researcher/marketer to uncover insights that may otherwise remain hidden.

Tailoring sampling methods to research goals

Different approaches are needed for each of the various areas of research, with respect to population structure and goals:

In market research, stratified sampling – in which age, gender, or location delineates the stratum – helps ensure all customer types are represented. Even a small representative group can reveal trends that might take years to notice in the whole population, such as the emerging preference for eco-friendly products. 

By the way, you can use a helpful tool as a market research platform like Checkbox to collect data from a representative sample, analyze patterns in real time, customize surveys, and more.

In health studies, random sampling from hospital records ensures that no hospital or region dominates. The inclusion of patients from rural or urban areas may reveal unexpected differences in the efficacy of treatments.

In academic research, stratification by major, year, and gender helps capture the complete diversity of the student population. Only a survey among successful students will allow the problems of the majority to go unnoticed.

In political polling, random-digit dialing or online surveys weighted by demographics provide accurate predictions. Even a small but important group (like first-time voters) can greatly influence the forecast if accounted for correctly.

Careful selection and structuring of the sample can give a complete and accurate picture, reveal hidden patterns, and provide vital insights for both researchers and businesses.

Final thoughts

A representative sample forms the very foundation of reliable research. To get the research outcomes, define your population, select the right sampling method, then calculate the right sample size, and check for the representativeness of the sample. Following these steps, you can reduce bias and make informed decisions. 

Apply representative samples in business, healthcare, or academic research to discover patterns in groups, understand trends, and predict outcomes (for example, student success or voting outcome).

If you're looking to make survey creation easier and ensure representative sampling, tools like Checkbox are incredibly useful. With Checkbox's no-code editor, building surveys with your own templates and multiple question options is a breeze. 

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