Synthetic Research Data
Synthetic Research Data explained
The purpose determines which features need to be retained. File structure and plausible values may suffice for an import test. Substantive analysis requires assessing whether the relationships relevant to that question are adequately represented.
A model simulating a target group’s answers is not interviewing that group. Its output may help formulate hypotheses or explore a questionnaire. Agreement with a campaign idea does not establish actual audience acceptance.
Label provenance, generation method and limitations in both dataset and report. Do not merge simulated and collected responses into an apparently larger real sample. More artificial records do not add independent observations.
The ONS notes that synthetic data do not preserve every feature of the source data and that disclosure risks need assessment. Suitability for a particular use requires its own evidence.
Examples
Hypothetical application
A team tests a planned questionnaire pipeline using artificial records: empty fields, different answer combinations and unusual values. This helps identify technical errors before fieldwork. Claims about purchase intent are based separately on appropriate real data.
Key Points
- Distinguish simulation from collected observation.
- Assess suitability for the specific research purpose.
- Artificial answers do not enlarge a real sample.
- Document provenance and limitations.
Practical application
Start with the intended use. Choose relevant test properties, document generation and assess the result against known requirements. Separate technical test findings from conclusions about a real market.
Useful measures
Fitness for purpose
Match with the properties required for the specific task.
Test coverage
Technical scenarios covered and remaining gaps.
Traceability
Documented provenance, generation and permitted interpretation.
Common mistakes
- Presenting simulated agreement as actual audience preference.
- Combining artificial and real responses without labelling.
- Assuming complete anonymity from the word synthetic.
Sources and context
- Office for National Statistics: Synthetic data policy
Methodological limits of synthetic data and assessment of disclosure risks; an ONS policy.
Frequently Asked Questions about Synthetic Research Data
Not categorically. They can support testing and development. Their suitability for claims about actual attitudes or behaviour needs separate evidence.
No. The generation method and source data affect possible disclosure risks. The label does not replace assessment.
A bounded technical test with a known expected result. Record which properties the test data represent and which conclusions remain out of scope.
Related links
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