We Tell You What We Don't Know

Most synthetic data providers deliver results without quality transparency. At Simsurveys, we believe honest uncertainty builds more trust than false certainty. Our advanced quality assurance system proactively identifies and flags questions where synthetic responses may be unreliable—before you make business decisions based on the data.

Quality Transparency Philosophy: We'd rather flag a potentially problematic question and maintain your trust than deliver questionable data without warning. This approach has consistently increased client confidence in our overall results.

Multi-Layer Quality Assurance Process

Every synthetic dataset undergoes comprehensive quality monitoring through multiple validation layers:

1

Contextual Analysis

Mutual Information (MI) and semantic measurements identify context questions that inform each simulation.

2

Statistical Generation

Multiple methodologies generate synthetic responses with built-in consistency and validation checks.

3

Distribution Analysis

Every question's response distribution is analyzed for statistical anomalies and logical consistency.

4

AI Outlier Detection

Claude API analyzes distributions to identify nonsensical patterns that statistical tests might miss.

5

Client Flagging

Problematic questions are clearly marked with confidence scores and recommendations for interpretation.

AI-Powered Outlier Detection

Our quality assurance system uses advanced AI to identify response patterns that may indicate insufficient training data or questions unsuitable for synthetic generation:

Semantic Coherence Analysis

AI evaluation examines whether response distributions make logical sense given the question context and expected human behavior patterns.

Cross-Question Consistency

Automated detection of responses that violate known relationships between demographics, attitudes, and behaviors.

Training Data Sufficiency

Identification of questions where limited training examples may compromise response quality, even when statistical metrics appear acceptable.

Pattern Recognition

Machine learning detection of subtle anomalies in response distributions that indicate potential simulation issues.

Question Confidence Scoring

Every question in your synthetic dataset receives a confidence score and, where relevant, specific guidance:

Confidence Categories:

  • High Confidence (Green): Question passes all quality checks with strong statistical validation
  • Medium Confidence (Yellow): Question meets statistical thresholds but shows minor anomalies worth noting
  • Low Confidence (Red): Question flagged for review due to outlier patterns or insufficient training data

Flagged Question Guidance: When we identify potential issues, you receive specific recommendations on how to interpret results, whether to supplement with live data, or if the question type may be unsuitable for synthetic generation.

Why This Builds Trust

Counterintuitively, our willingness to flag uncertain results increases client confidence in our overall data quality:

Transparent Limitations

By clearly identifying where synthetic data may be less reliable, clients trust our results more when we express high confidence.

Informed Decision-Making

Clients can make strategic choices about which results to act on immediately and which to validate with supplementary live data.

Quality Partnership

Our proactive quality guidance positions us as a research partner, not just a data vendor.

Continuous Improvement

Feedback on flagged questions helps improve our models and identifies patterns for future enhancement.

Research Methodology Impact

This quality assurance approach represents a significant advancement in synthetic data methodology:

  • No Test Data Required: Quality assessment works even when no validation dataset exists
  • Question-Level Granularity: Identifies specific problematic items rather than dismissing entire surveys
  • AI-Enhanced Detection: Combines statistical analysis with semantic understanding for superior anomaly detection
  • Client-Centric Communication: Translates technical quality metrics into actionable business guidance

This methodology ensures that synthetic data becomes a reliable research tool with clear boundaries and limitations—exactly what professional researchers need for confident decision-making.