Representative Sample: Definition, Importance, and Examples

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Updated October 29, 2021
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What Is a Representative Sample?

A representative sample is a subset of a population that seeks to accurately reflect the characteristics of the larger group. For example, a classroom of 30 students with 15 males and 15 females could generate a representative sample that might include six students: three males and three females. Samples are useful in statistical analysis when population sizes are large because they contain smaller, manageable versions of the larger group.

Key Takeaways

  • A representative sample is one technique that can be used for obtaining insights and observations about a targeted population group.
  • A representative sample is a small subset group that seeks to proportionally reflect specified characteristics exemplified in a target population.
  • For large surveys, researchers may divide the population into strata according to ethnic markers, gender characteristics, age groups, income, or geographical locations to ensure that they are using a representative sample.
  • In the U.S., the Census Bureau conducts several surveys of the business and demographic environments, using representative samples to ensure accurate coverage.
  • Representative samples often yield the best results but they can be the most difficult type of sample to obtain.
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Representative Sample

Understanding Representative Samples

Sampling is used in statistical analysis methodologies to gain insights and observations about a population group. Statisticians can use a variety of sampling methods to build samples that seek to meet the goals of their research studies. Representative samples are one type of sampling method. This method uses stratified random sampling to help identify its components. Other methods can include random sampling and systematic sampling.

A representative sample seeks to choose components that match with key characteristics in the entire population being examined.

Statisticians can choose the representative characteristics that they feel best meet their research goals. Typically, representative sample characteristics are focused on demographic categories. Some examples of key characteristics can include sex, age, education level, socioeconomic status, and marital status. Generally, the larger the population being examined, the more characteristics that may arise for consideration.

Types of Sampling Methods

Choosing a sampling method can depend on a variety of factors. Representative samples are usually an ideal choice for sampling analysis because they are expected to yield insights and observations that closely align with the entire population group.

When a sample is not representative, it can be known as a random sample. While random sampling is a simplified sampling approach, it comes with a higher risk of sampling error which can potentially lead to incorrect results or strategies that can be costly. Random sampling can choose its components completely at random, such as choosing names randomly from a list. Using the classroom example again, a random sample could include six male students.

Systematic sampling is another type of sampling method that seeks to systemize its components. This type of sampling may include choosing every fifth person from a population list to gather a sample. While this method takes a systematic approach, it is still likely to result in a random sample.

Stratified Random Sampling

Stratified random sampling can be an important part of the process in creating a representative sample. Stratified random sampling examines the characteristics of a population group and breaks down the population into what is known as strata. Dividing out the population by strata helps an analyst to easily choose the appropriate number of individuals from each stratum based on proportions of the population. While this method is more time consuming—and often more costly as it requires more upfront information—the information yielded is typically of higher quality.

One example of representative sampling is the American Community Survey, an annual study of the nation's key demographic characteristics. In order to ensure a representative sample, the Census Bureau stratifies the population according to county, living situation, and other demographic features. This ensures that the sampled population is an accurate snapshot of the population as a whole.

Special Considerations

A representative sample is generally expected to yield the best collection of results. Representative samples are known for collecting results, insights, and observations that can be confidently relied on as a representation of the larger population being studied. As such, representative sampling is typically the best method for marketing or psychology studies.

While representative samples are often the sampling method of choice, they do have some barriers. Oftentimes, it is impractical in terms of time, budget, and effort to collect the data needed to build a representative sample. Using stratified random sampling, researchers must identify characteristics, divide the population into strata, and proportionally choose individuals for the representative sample.

In general, the larger the population target to be studied the more difficult representative sampling can be. This method can be especially difficult for an extremely large population such as an entire country or race. When dealing with large populations it can also be difficult to obtain the desired members for participation. For example, individuals who are too busy to participate will be under-represented in the representative sample. Understanding the pros and cons of both representative sampling and random sampling can help researchers select the best approach for their specific study.

What Is the Best Way to Avoid Sampling Bias?

The simplest way to avoid sampling bias is to use a simple random sample, where each member of the population has an equal chance of being included in the sample. While this type of sample is statistically the most reliable, it is still possible to get a biased sample due to chance or sampling error.

How Do You Ensure a Representative Sample?

In order to ensure that their are sampling an accurate cross-section of the population, researchers will sometimes use systematic or stratified sampling methods to ensure that the sample resembles the known features of the population. For example, if the subject population is 55% male and 45% female, they will deliberately choose a sample that is 55% male. Note that this method requires the researchers to have a certain level of certainty about the population being sampled.

What Are the Downsides of Representative Sampling?

Like other statistical tools, representative sampling can sometimes produce biased or inaccurate results. Moreover, the time and cost of creating a representative sample may be prohibitive, especially if the population is spread out over a wide geographical area. There may also be self-selection bias, if some members of the sample fail to respond to the researchers' attempts to make contact.

The Bottom Line

A representative sample is a statistical snapshot that is used to make inferences about a wider population. While it is possible to get accurate results from a simple random sample, a representative sample is one that shares the same demographic characteristic of the larger population. These samples are more difficult to create but can lead to more accurate analysis, especially for a large study.

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  1. Census Bureau. "American Community Survey."