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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsValidate synthetic data against the task it must support—not against a generic similarity score. Start with schema and domain rules, compare the statistical features and analytical or testing outcomes that matter, and assess privacy risk separately from usefulness. A dataset suitable for exercising software may still be unsuitable for estimating outcomes or informing decisions.
1. Define what the data need to do
Write down the intended use before choosing validation measures. Code-path testing, exploratory analysis, population estimates and subgroup comparisons place different demands on the data. Identify the outputs, behaviors or decisions the dataset must support, then set acceptance criteria for those specific requirements. The UK Office for National Statistics (ONS) says fitness depends on the purpose and how the synthetic data were produced; it also cautions that high-quality analytical work may require real data (ONS Synthetic data policy).
Keep the intended use narrow enough to test. Passing checks for one task does not establish that the same dataset is appropriate for another.
2. Check structure and domain validity
First establish whether the records can be used at all. Validate the expected schema and the rules that make sense for the subject area:
#1 Best Overall
- Expected columns, data types, formats and permitted null behavior.
- Key constraints, uniqueness assumptions and valid ranges.
- Cross-field consistency and impossible combinations—for example, the ONS cites “no employed infants” as a validity check.
These checks catch malformed or contradictory records, including problems that can break routine software tests. They do not show that the data have realistic distributions or preserve relationships in the source. Treat validity and statistical fidelity as separate checks (ONS Synthetic data policy; ONS, Synthetic data: estimation of measurement error).
3. Compare the properties the task depends on
Where access rules allow, compare the synthetic data with a suitably protected real-data reference. Choose measures based on the analysis rather than applying a single score to every use case. ONS notes that synthetic data can preserve some source properties while failing to preserve others (ONS, Synthetic data: estimation of measurement error).
Rank #2
- Distributions and counts: compare important variable distributions, subgroup sizes and cell counts.
- Relationships: examine correlations and multivariate patterns required by the analysis.
- Estimates: compare relevant group means, model parameters or other estimates.
- Subgroups: inspect results for populations that matter to the intended use, not just overall averages.
The UK Financial Conduct Authority (FCA) distinguishes broad statistical comparisons from narrower comparisons of model or analytical performance. A dataset can look similar on broad measures yet fail to answer the particular question (FCA, Synthetic data: a guide). Set tolerances in terms of consequences: a discrepancy in a decision-critical subgroup may matter more than a larger mismatch in a feature irrelevant to the task. There is no universal similarity threshold established by the cited guidance.
4. Run the actual analysis or test
For analytics
Run the intended estimators or models on synthetic and reference data, when permitted. Compare the outputs, uncertainty and subgroup results that could affect a conclusion. If those differ materially for the intended decision, the dataset has not demonstrated utility for that use—even if its broad statistical profile appears close.
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For software and system testing
Decide whether the test needs only well-formed records that satisfy rules, 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 so that artifacts introduced by generation are not mistaken for real effects (NIST SP 800-188, De-Identifying Government Datasets).
5. Evaluate privacy independently of utility
Do not treat “synthetic” as a privacy guarantee. Review how the data were generated and protected, then assess disclosure or re-identification risks for the intended access and sharing context. High fidelity can preserve combinations associated with actual people (UK Statistics Authority, Ethical considerations in the use of synthetic data for research and statistics).
NIST SP 800-226 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 SP 800-226, Guidelines for Evaluating Differential Privacy Guarantees, March 2025). Privacy and utility are distinct dimensions with trade-offs: assess both for the specific release or access arrangement. NIST SP 800-188 states that faithfully representing all properties of the source while enforcing strong privacy guarantees is impossible (NIST SP 800-188, September 2023).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Record the validation boundary
Keep a concise record of what was generated and what the checks established. Include:
- The generator or method, provenance, and dataset version or date.
- Intended uses and uses that are unsupported or prohibited.
- Reference comparisons and task-level tests performed, with their outcomes.
- Known failures, subgroup limitations, and privacy assessment.
ONS recommends explaining how synthetic data were produced and which uses they may or may not suit (ONS Synthetic data policy). For consequential findings, include a controlled route to validation against real data when permitted; generated records can introduce uncertainty, underrepresent subpopulations or propagate bias (NIST SP 800-188).
How to compare candidate datasets or generators
When choosing between options, assess each against the same task-specific criteria rather than naming a universal winner.
| Comparison axis | What to examine |
|---|---|
| Validity | Schema, domain rules, ranges, keys and cross-field consistency. |
| Fidelity | Distributions and relationships needed for the stated task. |
| Utility | Performance on the intended analyses, queries or test outcomes. |
| Subgroup performance | Whether important populations are represented adequately for the use. |
| Privacy assurance | Generation safeguards and disclosure-risk assessment for the access context. |
| Reproducibility and documentation | Provenance, method, versioning, validation record and declared limits. |
These dimensions reflect guidance from ONS, the FCA and NIST; none of the cited sources establishes one threshold or generator that is best for every use (ONS; FCA; NIST SP 800-226).
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