Every numeric or ranked result generated by Social Explorer AI includes a methodology section displayed beneath the table. This section explains how the data was produced, which official sources were used, and what users should consider when interpreting the results. Its purpose is to ensure transparency, accuracy, and responsible use of demographic data.
A typical methodology section appears directly below an AI-generated table and provides essential context about the dataset, its reliability, and any limitations that may apply.
What the Methodology Section Explains
The Source and Type of Data Used
The methodology identifies the survey, dataset, and table used to generate the result. Examples include ACS 2023 5-Year Estimates, ACS 2022 1-Year Estimates, the 2020 Decennial Census, EASI projections, or other curated administrative datasets. This allows users to verify the exact origin of the data and understand which collection program it comes from.
How the Data Was Collected and Processed
The section explains whether the values are derived from a sample-based survey, a full population count, or a modeled dataset. It may describe how multi-year estimates were combined, whether values were averaged across time, or if adjustments such as inflation correction were applied.
Statistical Reliability and Known Limitations
Survey-based datasets include sampling error. The methodology notes indicate when margins of error should be considered, when small geographic areas may have higher uncertainty, and when population thresholds affect data availability. It may also note limitations such as suppressed values, incomplete coverage, or known undercounts.
Appropriate Interpretation and Use
The methodology clarifies how the data should be interpreted and used. It may indicate whether the results are appropriate for comparison across geographies or years, or whether such comparisons should be avoided. When certain uses are not recommended, the explanation clearly states why.
Special Calculations or Transformations
If the AI applies additional calculations such as percentages, medians, rankings, growth rates, or inflation-adjusted values, the methodology section explains how these transformations were performed so users understand how derived values were produced.
Why This Matters
The methodology section ensures that every AI-generated result is fully documented and grounded in real, verifiable data. By understanding the data source, processing steps, and limitations, users can interpret results accurately and responsibly.
Together with the Sources panel and the <datasources> metadata block, the methodology section provides a complete and academically reliable explanation of how each AI answer was generated.