Overview
AI-generated responses can be useful, but they should always be reviewed critically. A response may be clear, organized, and confident while still being incomplete or inappropriate for the research question.
Researchers should treat StatWiseAI output as a draft or suggestion. The final decision remains with the user and, when appropriate, statistician, IRB, or data governance team.
The CHECK framework
Use the CHECK framework before relying on AI-generated advice.
C — Context fit
Does the response match the actual research question, population, dataset documentation, study design, and analytic goal?
Ask:
- Did StatWiseAI understand the outcome?
- Did it understand the main predictor or exposure?
- Did it account for the study design?
- Did it respond to the question I actually asked?
- Did it ignore any important detail?
H — Hidden assumptions
What assumptions did the AI make?
Ask:
- Did it assume the data are independent?
- Did it assume the outcome is continuous or binary?
- Did it assume a cross-sectional design when the data are longitudinal?
- Did it assume survey weights are unnecessary?
- Did it assume the goal is causal inference when the goal is prediction or description?
E — Evidence and standards
Is the response consistent with statistical principles, dataset documentation, and disciplinary standards?
Ask:
- Is the suggested method appropriate for the outcome?
- Is it appropriate for the design?
- Does it address missing data?
- Does it address measurement and coding?
- Does it distinguish association, prediction, and causation?
- Should a statistician or methodologist review this?
C — Code and computation
If code was generated, does it run and does it do what it claims to do?
Ask:
- Are the variable names correct?
- Are placeholder names clearly marked?
- Are weights, strata, PSUs, clusters, or repeated measures handled correctly?
- Are missing values handled appropriately?
- Does the code match the intended model?
- Did the user test the code on simulated or approved data?
K — Knowledge limits
What does StatWiseAI not know?
Ask:
- Did I provide enough context?
- Did I leave out important design information?
- Is there information in the dataset documentation that StatWiseAI has not seen?
- Are there data-use restrictions, IRB requirements, or study-specific rules that StatWiseAI cannot determine?
- Should I verify this with a biostatistician, IRB, or data governance office?
Common red flags
Be cautious when an AI response:
- Gives one recommendation with no alternatives.
- Does not explain assumptions.
- Ignores missing data.
- Ignores survey design.
- Ignores clustering or repeated measures.
- Provides code with variables that do not exist.
- Makes causal claims from observational data.
- Gives a final interpretation before results have been checked.
- Overgeneralizes from a specific sample.
- Uses language that could stigmatize a group.
- Does not mention limitations.
Example: survey data red flag
A user asks:
“I am using NHANES documentation to plan an analysis of food insecurity and depressive symptoms. What regression should I run?”
A weak response might recommend ordinary logistic or linear regression without mentioning survey design.
A stronger response should ask about survey weights, strata, PSUs, cycles, eligibility criteria, outcome coding, covariates, and whether the user is estimating population-level associations. CDC’s NHANES tutorial emphasizes that weights are used to account for complex survey design, nonresponse, and post-stratification, and that weights are needed for estimates representative of the U.S. civilian noninstitutionalized population. CDC also notes that complex survey analysis needs information on strata and PSU variables for variance estimation.
Example: longitudinal data red flag
A user asks:
“I am using HRS documentation to study change in functional limitations over time. Can I use a simple regression model?”
A weak response might say yes without discussing repeated measures.
A stronger response should ask about waves, timing, attrition, within-person correlation, time-varying variables, and whether the goal is describing change, estimating associations, or making predictions. HRS has a longitudinal cohort sample design, with members of the initial cohort interviewed every two years since 1992.
Useful follow-up prompts
After receiving an AI response, users can ask the AI follow-up questions (i.e., follow-up prompts) such as:
What assumptions are you making?
What information is missing from my prompt?
What could make this analysis plan inappropriate?
What would a statistical reviewer question?
What sensitivity analyses should I consider?
How would this recommendation change if my outcome is binary rather than continuous?
How would this recommendation change if I am using survey weights?
How would this recommendation change if I have repeated measures?
What should I verify in the dataset documentation before proceeding?
Bottom line
A useful AI response should help the researcher think more clearly. It should not end the analytic decision-making process.
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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