The Science Behind Synthetic Data
Simsurveys represents a fundamental shift in survey methodology, moving from traditional panel recruitment to AI-generated synthetic respondents. Our approach is grounded in rigorous statistical validation and domain-specific modeling that maintains the analytical integrity researchers expect.
Key Innovation: Domain-engineered models trained on millions of validated responses, not general-purpose language models adapted for surveys.
Unlike generic AI systems, our synthetic respondents are built from carefully curated datasets within specific research domains. Each model understands the nuanced response patterns, demographic correlations, and behavioral consistency that characterize real human survey data.
Validation Framework
Every synthetic dataset undergoes comprehensive validation against live panel benchmarks. Our testing framework evaluates:
- Statistical Distribution Alignment: Chi-square and Kolmogorov-Smirnov tests to ensure response distributions match real panel data
- Demographic Consistency: Cross-tabulation analysis to verify that synthetic respondents maintain realistic demographic correlations
- Response Pattern Fidelity: Validation that synthetic respondents exhibit human-like response consistency and logical patterns
- Domain-Specific Accuracy: Benchmarking against known population parameters in healthcare, consumer, and social research domains
In controlled studies, Simsurveys typically achieves 80-90% alignment with high-quality panel results, significantly outperforming low-quality web intercepts and rushed panel studies.
Three-Domain Architecture
Rather than attempting to build one model for all research, we've developed specialized models for distinct research domains:
Healthcare & HCP Research
Built on physician-level databases with prescription behavior patterns, medical specialty correlations, and clinical decision-making frameworks.
Validation: Benchmarked against major medical panel providers
Consumer & Market Research
Trained on consumer behavior patterns, purchase intent correlations, and demographic-psychographic relationships from validated market research studies.
Validation: Compared to Nielsen, GfK, and other tier-one panel providers
Social & Political Research
Incorporates voting behavior, social attitudes, and political opinion formation patterns with careful attention to demographic and geographic correlations.
Validation: Tested against census data and major polling organizations
Research Documentation
Our research foundation is built on transparent methodology and published validation studies. Explore the academic and industry research that supports our approach:
Mode Effects Research
Historical analysis of how survey methods have evolved and validation studies comparing synthetic data to traditional panel approaches.
Read Mode Effects Studies →AI vs Human Prediction
Cognitive psychology research and comparative studies examining how well AI models predict human survey responses.
Review Prediction Research →Validation Studies
Statistical validation studies, case studies, and benchmarking reports that demonstrate the accuracy and reliability of synthetic data.
View Validation Results →Methodological Transparency
We believe in methodological transparency. Our approach includes:
- Published Limitations: Clear documentation of where synthetic data performs well and where caution is needed
- Confidence Intervals: Statistical uncertainty measures for all synthetic datasets
- Domain Boundaries: Explicit guidance on which research questions are suitable for synthetic respondents
- Validation Reports: Detailed benchmarking results for each domain and research type
Patent Protection: Our methodology is protected by U.S. Patent Application No. 18/784,418 and additional provisional patents, ensuring the integrity and uniqueness of our approach.