Non-Sampling Error
Overview
A non-sampling error is any error that arises during data collection, processing, or analysis that causes results to differ from the true values and is not attributable to the fact that only a sample (rather than the entire population) was observed. Unlike sampling error, which results from the inherent variability when selecting part of a population, non-sampling errors stem from external or procedural problems and can affect both samples and full censuses.
Key points
- Non-sampling errors include random and systematic errors; systematic errors are more serious because they bias the entire dataset.
- Increasing sample size reduces sampling error but does not reduce non-sampling error.
- Common sources include flawed questionnaire design, respondent mistakes or deception, interviewer bias, coverage problems, and processing or coding mistakes.
- High rates of non-sampling error undermine reliability and can force redoing a study if bias is pervasive.
Types and examples
- Random non-sampling errors
- Small, unpredictable mistakes (e.g., occasional data-entry typos) that tend to offset over many observations.
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Usually do not require discarding the study but should be minimized.
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Systematic non-sampling errors
- Consistent biases that skew results in one direction (e.g., a leading survey question, a measurement device consistently miscalibrated).
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Can render results invalid and may require repeating the study.
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Specific forms
- Response errors: respondents give incorrect or intentionally false answers.
- Nonresponse errors: certain individuals do not participate, creating bias if nonrespondents differ from respondents.
- Coverage errors: elements of the target population are omitted or counted multiple times (e.g., faulty sampling frame).
- Interviewer errors: interviewer wording, prompts, or behavior influence answers.
- Processing errors: mistakes in coding, data entry, editing, or analysis.
Consequences
- Systematic non-sampling errors increase bias and reduce the validity of conclusions.
- Even small systematic errors can change study outcomes or policy recommendations.
- Because they are often hard to detect, transparency about methods and quality checks is essential.
Mitigation strategies
- Design and testing
- Pilot surveys and cognitive testing of questions to detect ambiguity or bias.
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Use clear, neutral wording and standardized procedures.
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Sampling frame and coverage
- Maintain and validate sampling frames to avoid omissions or duplicates.
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Use multiple sources to improve coverage where feasible.
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Response management
- Reduce nonresponse with follow-ups, varied contact modes, and appropriate incentives.
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Track nonresponse patterns and adjust with weighting or imputation when justified.
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Interviewer and field control
- Train interviewers thoroughly and monitor fieldwork for protocol adherence.
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Use supervision, spot checks, and audio/video verification where appropriate.
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Processing and quality control
- Implement double data entry, automated range and consistency checks, and audit trails.
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Document coding rules and transformations; use reproducible analysis workflows.
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Transparency and validation
- Report response rates, error checks, and known limitations.
- Where possible, validate results against independent data sources.
Summary
Non-sampling errors pose a major threat to data quality because they can introduce bias that is not corrected by larger sample sizes. Identifying, minimizing, and documenting these errors through careful design, rigorous field procedures, and robust processing controls are essential to producing reliable, defensible results.