When your research sample doesn’t match the target population, skewed results can sink strategic decisions. Getting insights on the wrong mix of people leads you down wayward paths, and who wants to get lost? Not me! That’s why it’s important to avoid unrepresentative samples in your research so you can stay on the right road to success.
You’ll miss nuances dividing segments without demographic and behavioral diversity in your data. And if your sample misses the mark, you’ll end up with an unrepresentative sample. But thoughtful quota setting and sampling allow the true voice of segments to emerge in harmony. When done well, it orchestrates discoveries that are not possible through unrepresentative samples. Your decisions deserve the backing of accurate insights – representative sampling lays that foundation.
In this article, I discuss:
- Representative sample definition
- The importance of getting a representative sample
- How an unrepresentative sample can occur
- Ensuring a representative sample
- The size of a representative sample
- Correcting an unrepresentative sample
What is a Representative Sample?
A representative sample is a subset of a population that accurately reflects the members of the entire population on key characteristics. Often, the goal is for the sample to be a microcosm of the population from which it is drawn, such that the researcher can draw inferences about the population based on analyzing patterns in the sample.
Some key aspects in the definition of a representative sample include:
It is drawn from the target population the research aims to describe. For example, if you want to understand soda drinking habits across the U.S., your sample should consist of U.S. residents.
The distributions and proportions of key demographics or other relevant characteristics match the true distributions in the target population. For example, a U.S. representative sample would need 51 percent females, and 49 percent males to match Census distributions of gender.
All relevant subgroups are represented in reasonable proportion to their true share of the total population.
The closer a sample maps to your relevant population benchmarks, the more confidence there is that analyses on behaviors, attitudes, and opinions can be generalized to the target market. In some cases, researchers strive for a mini-version of the full population census data demographics. The U.S. Census is considered the “gold standard” in representative sampling for understanding the nation as a whole.
However, sometimes researchers are interested in just a subset of the population, such as millennials or crossover SUV owners. In that case, representative sampling refers to creating a miniature version of THAT target population. Researchers need to define upfront what total pool of people they want to represent.
Read next: From Unknowns to Insights: Primary Research Benefits Include Informed Decisions
Why is Using a Representative Sample Important in Research?
Using a sample that represents the target population well is critically important for producing quality research that can be relied upon for decision-making. Without proper representation, there is too much risk of bias-skewing results and invalid findings.
Some reasons why representative sample matters include:
Ensures subgroups are captured. They may behave or think differently. Without proper representation, some segments could be completely missed leading to a biased dataset. For example, Hispanics could view a product very differently, but a sample with no Hispanics would completely miss that.
They allow for segment-level analyses. Representative samples ensure reasonable sample sizes within segments like age brackets, regions, etc., so differences can be analyzed.
With a representative sample, there’s a high confidence that patterns in the sample data reflect the total population patterns, allowing for projections.
The bottom line is that with proper representation across the relevant consumer population, researchers can have much higher confidence in the quality and generalizability of the research findings.
Ways That Samples Become Unrepresentative of the Target Population
There are a variety of ways samples may end up failing to accurately reflect the characteristics of the target population:
No Quotas Set Whatsoever: The most obvious issue arises when no targeting or quotas are set based on demographics, behavior, or any other relevant traits. Convenience sampling with no structure beyond very basic criteria allows too much variability in who ends up in the final sample relative to the population.
Not Setting Quotas to Population Benchmarks: Without mirroring a population, it’s difficult for a sample to be truly representative.
Flawed Benchmark Data: In some cases, researchers may aim to match a sample to a benchmark, but the benchmark data is faulty. So the quotas and final sample distribution end up being wrong. Ensuring high-quality population data is key.
Underrepresentation of hard-to-reach groups: Some consumer segments are more difficult for researchers to access or recruit to studies. For example – youth, ethnic minorities, and senior citizens tend to be more costly to recruit. Budget limitations can lead to short-changing these groups.
Maintaining representativeness requires actively mitigating potential biases through quota setting.
Ensuring A Representative Sample
When it comes to the types of sampling methods, the best practice for ensuring a sample mirrors the population on key qualifying traits is to set formal quotas for recruitment. Essential steps in effective quota setting include:
Identify Target Population
First, define exactly what base population you want the sample to represent. Get clear on the group you are trying to generalize findings to.
Determine Key Factors
Decide what criteria are most relevant to representing that target group. Common elements include – age, gender, ethnicity, region, socio-economics, purchase behavior, etc.
Review Population Distributions
Gather Census, panel profile, past survey, or other total market data on true population distributions for the profiling factors identified. These become quota benchmarks.
Set Quota Requirements
Map out quota cells and allocation rules to mimic true distributions as closely as possible. For example – 51 percent females, 72 percent Caucasian, 15 percent West Region, etc.
Assign Quotas
Embed quotas into research instrumentation to cap or balance progress.
Monitor
Continuously examine achievement against quotas and adapt invites, surveys in the field, etc.
Setting formal quotas aligned to population benchmarks and dynamically managing progress towards filling them is essential to representative sampling. Shortcuts result in the risk of distortions. Dedicating effort to optimizing quotas and balancing response rates across groups enables representativeness.
Key questions to consider to get a representative sample
Some key questions clients should think about include:
- What consumer groups represent our target market and require representation (e.g., millennials, suburban mothers)?
- What specific product/brand do we need the sample to resonate with and reflect buyers of (e.g., outdoor equipment customers)?
- What demographic factors like age, ethnicity, and location critically impact consumer behavior and need quota alignment?
- Are there key behavioral traits like usage frequency, shopping habits, or media consumption that require representation (e.g., digital news readers)?
- Do certain psychographic factors like attitudes, lifestyle, or tech-savviness play a strong role in segmentation and need reflection in the sampling (e.g., sustainability beliefs)?
Spelling out key qualitative and quantitative traits that accurately profile the target groups they want findings generalized to is critical for clients that have representativeness expectations. This clarity then allows research suppliers to implement the necessary sampling design and quotas. Without such specificity, what constitutes a “representative sample” remains undefined and risks misalignment.
How Large Does a Sample Need to Be?
In most cases, having a sufficiently large sample size is a necessary prerequisite to achieving representativeness in practice. But how many completes are really needed? Rules of thumb on minimum sample sizes include:
- For a simple survey with all respondents seeing the same instrument, a sample size of 200+ completes is often sufficient.
- If segment-level analyses will be run comparing subgroups, each segment requires ~100 completes.
- To increase the confidence level and spot differences, that often requires 500+ completes depending on assumptions.
- For representing a broad national population, sample sizes of 1000+ are common for quotas to stabilize a margin of error.
- For a narrower, more targeted audience, the sample size can be smaller as there is less variability in the niche group
What Kinds of Biases Can Be Introduced from an Unrepresentative Sample?
Myriad inherent biases around behaviors, attitudes, and opinions can creep into the data set when samples reflect target groups poorly. Common distortions that unrepresentative samples risk include:
Segmentation blind spots: Missing a key consumer cluster that interacts very differently with products and failing to represent them leads to losing sight of that segment’s perspective.
Inaccurate Product Testing Results: Reactions differ markedly for certain product attributes across consumer cohorts, so distorting the mix can swing impressions.
Unreliable Brand Metrics Tracking: Brand health metrics lift/decline can change based on consumer type.
While often subtle and hard to recognize from the data alone, lack of representation allows results to stray meaningfully from the real answers. Guarding against these implicit errors through rigorous sampling practices is crucial.
How Can Researchers Correct for Limitations in their Sample?
Despite best efforts, slips in representative sampling can occur. So when it comes to how to avoid an unrepresentative sample in this case, here are some tips:
Weighting
Mathematically adjusting underrepresented groups through over-weighting and vice versa to rebalance responses to known distributions.
Augment Surveys
Actively boost completion counts through targeted solicitations from subsets found to be lagging via quotas midway through research.
Panel Enhancement
Go beyond existing partner panels by recruiting specialty respondents from alternate providers to fill sample gaps.
Establish Baselines
Collect profiling data on all panelists/volunteers initially so can monitor sample distortions versus population even if not correcting.
Conclusion
Achieving representative sample reflecting target populations accurately poses an intricate challenge in the research landscape. But with so much riding on sound sampling, researchers must remain vigilant to gaps through every project phase. Let’s avoid the unrepresentative sample so your research can drive results!
