Validate synthetic data against the specific task it must support—not just whether its records look realistic. Check structure and domain rules, compare task-relevant statistics, run the intended analysis or tests, and assess privacy risk separately from analytical usefulness. A dataset can be valid for exercising software yet unsuitable for estimating outcomes or guiding decisions.
Start by defining what the data must do
Write down the intended use before choosing validation measures. Code-path and system testing, exploratory analysis, population estimates, and subgroup analysis place different demands on a dataset. Identify the outputs or decisions the data must support, then set acceptance criteria that relate to those outputs.
Fitness depends both on the purpose and on how the data were generated. The Office for National Statistics (ONS) recommends assessing synthetic data for fitness to purpose and cautions that high-quality analytical work may require real data. Its Synthetic data policy also says, “Synthetic data should be expected to contain errors and differences.”
Validate in six steps
1. Check structure and domain rules
Confirm that the dataset has the expected columns, data types, formats, keys, ranges, null behavior, and uniqueness properties. Test cross-field rules and impossible combinations as well. For example, ONS identifies “no employed infants” as a domain validity check.
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Keep these checks distinct from statistical comparison: records can satisfy a schema and contain plausible values while still having misleading distributions or relationships. Structural validity is necessary for use, but it does not prove analytical usefulness.
2. Compare statistics that matter to the task
Where access rules permit, compare synthetic data with a suitably protected real-data reference. Select measures based on the planned work, which may include:
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- Univariate distributions and important ranges.
- Subgroup sizes and cell counts, especially for groups that affect decisions.
- Group means or other estimates the analysis will report.
- Correlations and multivariate relationships required by the analysis.
- Model parameters or inference results when those are central to the task.
A synthetic dataset may preserve some properties and fail to preserve others. The ONS discusses this issue in its synthetic data guidance. The Financial Conduct Authority (FCA) distinguishes broad statistical comparisons from narrower comparisons of model or analytical performance in its synthetic data research paper.
Do not rely on one arbitrary similarity score as a universal pass. Set tolerances according to analytical consequences: a discrepancy in a critical minority group may be more consequential than a larger difference in an irrelevant marginal distribution.
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3. Run the intended analysis or test
For analytics, run the target estimators or models on both synthetic and reference data when permitted. Compare the outputs, uncertainty, and subgroup results that matter to the decision—not just a general resemblance score. Differences can show whether the synthetic data support the specific inference you intend to make.
For software testing, decide whether the test needs only correctly formatted, rule-valid records or also realistic distributions, relationships, and edge cases. Synthetic data can help develop queries and techniques before applying them to actual data; NIST advises validating discoveries against the original data to avoid mistaking generation artifacts for real effects.
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4. Examine subgroups and potential bias
Check whether important populations are adequately represented and whether errors differ across groups. Generation can reduce accuracy for subpopulations, add uncertainty, and propagate biases present in the source or generation process. An overall similarity result can conceal a failure that matters for a particular group.
5. Assess privacy independently
Review how the data were generated and what safeguards apply, then assess disclosure or re-identification risks for the planned access or release. Do not assume that synthetic records are safe simply because they are generated rather than directly copied. High fidelity can reproduce combinations associated with real people.
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NIST’s SP 800-226, published March 2025, warns that synthetic data without differential privacy may not provide robust protection against privacy attacks. Differential privacy can provide formal guarantees, but it does not by itself establish analytical utility. NIST also explains in SP 800-188, published September 2023, that “Constructing synthetic data that faithfully represent all properties of the original data while enforcing strong privacy guarantees is impossible.” Treat privacy protection and utility as separate dimensions in the same release decision.
6. Record the validation boundary
Document the generator or method, provenance, intended uses, unsupported uses, reference comparisons, known failures, privacy assessment, and the dataset’s date or version. Explain what the data can and cannot support. For consequential decisions, specify how results will be checked against real data or otherwise validated through controlled access when permitted.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare datasets or generators
When choosing between options, evaluate each against the same declared use. A dataset that performs well on one dimension may not lead on another.
| Comparison axis | What to check |
|---|---|
| Validity | Schema, data types, ranges, keys, and domain constraints. |
| Fidelity | Distributions, relationships, subgroup sizes, and estimates needed for the task. |
| Task utility | Performance on the actual analyses, queries, or tests the data are meant to support. |
| Subpopulation performance | Whether important groups or edge cases are represented adequately. |
| Privacy protection | Disclosure-risk assessment and the assurance provided by the generation method. |
| Governance | Reproducibility, provenance, and documentation of limitations. |
There is no universal threshold or single score that establishes fitness for every use. ONS, FCA, and NIST guidance all point toward evaluating synthetic data in relation to their intended application. If high accuracy is essential and no safe, sufficiently accurate synthetic alternative is available, controlled use of real data may be necessary. The UK Statistics Authority’s ethical guidance for trustworthy data ethics, published 19 October 2022, is relevant to the governance side of that decision.
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