A small sample can make an estimate imprecise; a biased sample can make it systematically unrepresentative. A large sample may still mislead if the wrong people were recruited, some groups were missed, or the questions shaped the answers. To judge a survey or poll, look beyond its headline number: check who it was meant to represent, how participants were selected, who did not respond, how questions were asked, and what uncertainty accompanies the result.
Small sample size and biased selection are different problems
Sample size mainly affects precision: with an appropriate sampling design, more observations generally reduce random variation in an estimate. Selection affects representativeness: if some members of the target population were more likely than others to enter the sample, the results can systematically miss or overstate their views.
Adding respondents can help with random sampling error, but it does not automatically fix a flawed sampling frame, volunteer recruitment, nonresponse, leading questions, inaccurate answers, or data-processing errors. The Office for National Statistics (ONS) distinguishes sampling uncertainty from nonsampling problems, which can remain even when a survey reaches many people. ONS guidance on uncertainty
There is no universal number of respondents that makes a study reliable. Adequacy depends on the population, how much the outcome varies, the sampling design, the precision needed, and whether the study makes claims about subgroups.
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Start with the population the claim is about
Write down the exact group the result claims to describe: for example, adults in a country, households in a city, current customers, or people with a particular condition. Then compare that group with the people who could actually enter the sample.
A survey of a company’s customers does not automatically describe all consumers. A social-media poll cannot automatically speak for people who did not use that platform or see the poll. A result about respondents is not necessarily a result about everyone in the target population. The Australian Bureau of Statistics explains that samples can be random or non-random and that a small sample may not represent the population as a whole. ABS guidance on census and sample
Check how people entered the sample
Look for the sampling frame—the list, register, platform, or other source from which participants could be selected—and how selection and recruitment worked. Ask whether people were selected through a probability design, where selection probabilities are known, or whether participation depended on opting in.
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- Probability-based selection: When implemented well, it provides a basis for estimating sampling variability for the target population covered by the design.
- Volunteer or opt-in recruitment: People who choose to participate may differ from those who do not. A large volunteer poll is not automatically representative.
- Weighting: Weighting can change respondents’ relative contributions to align measured characteristics with population benchmarks. To assess it, readers need to know which characteristics were weighted and whether the benchmarks fit the population and sample.
Weighting is not a general-purpose cure for people missing from the frame, unmeasured differences between respondents and nonrespondents, or inaccurate answers. AAPOR’s survey guidance describes the methodological details that help readers assess how a sample was assembled and adjusted. AAPOR Best Practices for Survey Research The U.S. Census Bureau also explains considerations in developing and implementing a sample design. Census Bureau Standard A3
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Look past the number invited or surveyed
Check the number of completed responses, not just the number invited, contacted, or who began a survey. People may be unreachable or may decline to participate, and those who do not respond may differ from those who do. Other nonsampling errors include inaccurate answers and mistakes in processing or analysis. These issues can affect a result even when a study attempts to survey every member of its defined population. ONS guidance on uncertainty
Method reports should make recruitment and response understandable. If they do not provide enough information to assess who was missed or how many completed the survey, treat that as an evidence limitation rather than assuming the sample reflects the target population.
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Read the questions, answer options, mode, and timing
A percentage is only as informative as the measurement behind it. Wording can steer respondents, while answer options can constrain what they can say. The survey mode and timing can also affect who participates and how they answer.
Look for the full question wording, response options, survey mode, field dates, target population, recruitment method, and any weighting. AAPOR recommends transparent reporting of these details so readers can judge what a survey actually measured. AAPOR Best Practices for Survey Research
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Interpret uncertainty without mistaking it for a bias check
For estimates from a probability sample, look for a suitable measure such as a standard error, confidence interval, or coefficient of variation, and note the method and confidence level. These measures describe sampling variability under the stated design; they do not automatically account for nonresponse, poor coverage, misleading questions, or other nonsampling errors. ONS explains that standard error indicates precision and that different samples could produce different estimates. ONS guidance on uncertainty
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Do not assume a conventional margin of error applies to every poll. AAPOR cautions journalists against reporting error margins for non-probability samples without an appropriate model. AAPOR’s journalist guide to polls and surveys
Statistical significance is not the same as effect size or practical importance. The U.S. Census Bureau’s Standard E1 says sample-based conclusions need appropriate measures of statistical uncertainty and notes that a p-value does not tell readers the size of an effect. Census Bureau Standard E1
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Be especially cautious with subgroup claims
A subgroup—such as respondents in one age range or region—usually contains fewer observations than the full sample, so its estimate may be substantially less precise. Before comparing subgroup percentages, check each denominator and its uncertainty. AAPOR advises journalists not to highlight differences within very small subgroups and to identify the subgroup behind any reported finding. AAPOR’s journalist guide to polls and surveys
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Compare studies on methods, not headline sample size
| What to compare | What to check |
|---|---|
| Target population and coverage | Whom each study aims to represent and who could enter its sampling frame. |
| Selection and recruitment | Whether selection was probability-based or non-probability, how participants were recruited, and how nonresponse was handled. |
| Measurement | Question wording, answer options, survey mode, and timing. |
| Precision | Completed sample size, design effects, uncertainty measure, and confidence level. |
| Subgroup support | The denominator and uncertainty for each subgroup finding. |
| Transparency | Whether the methods and weighting are documented well enough for an outside reader to assess them. |
A study with fewer respondents may be more informative for a particular population if its coverage, recruitment, measurement, and uncertainty are better documented. Headline sample size alone cannot settle which study deserves more confidence.
Keep conclusions within what the design can show
A survey can estimate characteristics or describe reported opinions, subject to its design and measurement limits. A descriptive percentage or association does not, by itself, establish why an outcome occurred. Do not turn a correlation or group difference into a causal explanation unless the research design supports that inference. The Census Bureau’s guidance on analyzing data addresses the need for appropriate statistical uncertainty when drawing conclusions. Census Bureau Standard E1
A historical example: judging a change against uncertainty
In an ONS data example, the estimated proportion of people aged 18 and over in the UK who were current smokers was 20.2% in 2011 and 14.7% in 2018. ONS reported that a statistical significance test found the difference larger than would be expected from random sampling alone. These are historical figures from the ONS’s 2019 example, not a current prevalence estimate or a universal test of whether a sample is sound. ONS guidance on uncertainty
Use this checklist before trusting or repeating a result
- What exact population does the claim concern?
- How were people or units sampled and recruited?
- Who was excluded, unreachable, or nonresponsive?
- What were the exact questions, answer options, survey mode, and field dates?
- What uncertainty measure fits the design, and is it reported for the subgroup being discussed?
- What potential nonsampling errors remain?
- Does the conclusion say only what the population, measurement, and design can support?
When essential methods are not reported, say that the result cannot be fully evaluated from the available information. Do not fill the gap by treating a large sample, a margin of error, or a confident headline as proof of representativeness.
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