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

Our auxiliary model (detailed in the next section) evaluates every question in your synthetic dataset and provides confidence assessments to help you understand which results are highly reliable and which may benefit from additional validation.

What You Receive:

  • Per-Question Confidence Metrics: Statistical measures indicating how closely each question's synthetic responses are expected to match real-world patterns
  • Quality Flags: Identification of questions where the model predicts lower accuracy or where training data may be insufficient
  • Interpretive Guidance: Clear recommendations on which results can be used with high confidence and which may require supplementary validation

Actionable Recommendations: When we identify potential concerns, you receive specific guidance on how to interpret results, whether to supplement with live data, or if the question type may be unsuitable for synthetic generation. This transparency ensures you can make informed decisions about how to use each component of your synthetic dataset.

Technical Foundation: The Auxiliary Model

Behind our confidence scoring system is a sophisticated auxiliary model—a confidence estimator that predicts how accurate the synthetic data generation model will be for any given survey question.

How the Auxiliary Model Works

After training our main model to generate synthetic respondents, we evaluate its performance by comparing the response distributions that emerge from those synthetic respondents against actual survey results. We measure prediction error using KL divergence—a statistical measure of how closely our synthetic population's responses match real-world patterns (see our Validation Studies page for a deeper KL explanation).

Validation and Practical Use

This auxiliary model functions as an automatic validation layer for synthetic data quality. When generating synthetic responses for any new survey, it outputs a per-question confidence score indicating how reliable each prediction is likely to be. Questions with high confidence can be trusted to match real-world distributions closely; questions with lower confidence can be flagged for additional review, alternative modeling strategies, or human oversight.

To ensure robustness, we train the auxiliary model on a balanced dataset that forces it to learn the distinction between easy and difficult items, then evaluate it on real-world question distributions. This two-stage pipeline allows you to:

  • Route routine, high-confidence questions through the main model with minimal overhead
  • Automatically identify questions that require enhanced validation before use
  • Maintain transparent, per-question quality control without manually evaluating every prediction

The result is a scalable system that not only generates synthetic data but also indicates how closely each output is likely to match live survey patterns—bringing both accuracy metrics and transparency to your workflow.

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.