Overview
StatWiseAI is designed to help researchers work with publicly available datasets rather than raw restricted datasets. This is especially important for biomedical, social, behavioral, educational, and health-related datasets, where data-use agreements, privacy rules, IRB requirements, or federal regulations may restrict if and how data can be shared.
Users should not upload proprietary data, sensitive data, participant-level data, PHI, HIPAA-regulated information, FERPA-regulated information, or other restricted information. Instead, users should work with materials that are public, approved, simulated, aggregated, or otherwise allowed under the relevant data-use policies.
What kinds of materials can be useful?
StatWiseAI can often help with analysis planning when users provide or describe:
- Public dataset documentation.
- Metadata.
- Data dictionaries.
- Codebooks.
- Variable descriptions.
- Survey documentation.
- Public analytic guidelines.
- Questionnaires.
- Statistical output.
- Analytic code.
- Simulated practice data.
- De-identified summary information, when allowed.
- Aggregated tables, when disclosure risk has been considered.
What StatWiseAI can help users do with documentation
StatWiseAI may help users:
- Understand how a dataset is organized.
- Identify candidate variables for a research question.
- Compare measures across survey waves or modules.
- Locate outcome, exposure, and covariate concepts.
- Recognize important design features.
- Identify missing data concerns.
- Create a preliminary analysis plan.
- Draft code using placeholder variable names.
- Prepare questions for a statistician or data expert.
- Document analytic decisions.
Example: reading a codebook
A user might paste a short public codebook excerpt or describe relevant variables and ask:
“I am reviewing public documentation for a national health survey. The codebook includes variables related to depressive symptoms, demographic characteristics, and health conditions. Help me identify which variables might be candidates for the outcome, exposure, and covariates in an analysis of depressive symptoms among adults. Do not assume these are final analytic choices. Provide a checklist of documentation items I should verify.”
A useful response should not simply choose variables. It should help the user think through measurement, coding, eligibility, missingness, survey design, and interpretation.
Example: using public HRS documentation
The Health and Retirement Study (HRS) describes itself as a longitudinal panel study of a representative sample of approximately 20,000 people in the United States, and its documentation page includes questionnaires, codebooks, data descriptions, and related study content.
A user might ask:
“I am using public HRS documentation to plan a longitudinal analysis of depressive symptoms and later functional limitations. I am not uploading participant-level data. Help me identify what types of HRS documentation I should review before selecting variables and models.”
StatWiseAI could help the user identify documentation areas such as:
- Questionnaires.
- Codebooks.
- Data descriptions.
- Survey design information.
- Weights.
- Cross-wave variable availability.
- Missing data and attrition information.
- User guides or documentation reports.
Example: using public NHANES documentation
The National Health and Nutrition Examination Survey (NHANES) provides access to questionnaires, datasets, documentation, and a data analysis tutorial, and CDC describes NHANES as the only national health survey that includes health exams and laboratory tests.
A user might ask:
“I am using public NHANES documentation to plan an analysis of food insecurity and depressive symptoms among adults. I am not uploading raw data. Help me identify relevant documentation, survey design features, possible variables, and common analysis mistakes to avoid.”
StatWiseAI could help the user think through:
- Questionnaire components.
- Examination and laboratory components.
- Demographic files.
- Variable search tools.
- Survey weights.
- Strata and primary sampling units.
- Combining survey cycles.
- Eligibility and age restrictions.
- Missing data.
- Subsample weights, if specialized measures are used.
Documentation review checklist
Before asking StatWiseAI for analysis recommendations, users should try to identify:
- What dataset or documentation source is being used?
- What population does the dataset represent?
- What years, waves, or cycles are relevant?
- What is the outcome variable?
- What is the exposure or main predictor?
- What covariates may be needed?
- Are survey weights required?
- Are there strata, PSUs, clusters, or repeated measures?
- Are variables measured consistently across time?
- Are there restricted variables that cannot be uploaded or described in detail?
- Are there missing data or attrition concerns?
- Are there official analytic guidelines?
Key reminder
Dataset documentation is not just administrative background. It is part of the analytic evidence. Good analysis depends on understanding how the data were collected, who is represented, how variables were measured, and what design features must be addressed.
Start Here: Responsible Use Rules
AI Basics for Researchers
Prompting for Data Analysis
Working with Dataset Documentation
Reviewing AI Outputs
Requesting Code
Reproducibility and Prompt History
Practice Use Cases: HRS and NHANES
Templates and Checklists
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