Overview
StatWiseAI can support code generation in R, Python, SAS, Stata, and SPSS. Users should choose the language that fits their project, training, data environment, and team workflow.
AI-generated code should always be treated as a draft. Users are responsible for reviewing, testing, revising, and documenting any code before using it in an actual analysis.
What to include in a code request
A strong code request should include:
- The preferred software language.
- The analysis goal.
- The type of outcome.
- The general model or analysis type.
- Placeholder variable names.
- Any design features, such as weights, strata, PSUs, clusters, or repeated measures.
- How missing data should be handled or flagged.
- The desired output.
- A request for comments explaining the code.
Use placeholder variable names
When requesting code, users should avoid entering sensitive or restricted data values. In many cases, users can use placeholder names.
Example:
Please draft R code for a survey-weighted logistic regression using placeholder variable names. Use OUTCOME for the binary outcome, EXPOSURE for the main predictor, AGE, SEX, RACE_ETHNICITY, and INCOME for covariates, WEIGHT for the survey weight, STRATA for strata, and PSU for the primary sampling unit. Include comments explaining what each placeholder should be replaced with.
General code request template
I need code in [R / Python / SAS / Stata / SPSS].
The goal is to [describe analysis goal].
I am not uploading sensitive or participant-level data.
Please use placeholder variable names only.
The outcome is [continuous / binary / count / time-to-event / repeated measure / other].
The main predictor is [description].
Covariates include [list].
Important design features include [survey weights / strata / PSU / clustering / repeated measures / longitudinal waves / missing data].
Please write commented code and include notes about assumptions, diagnostics, and what I should verify before running it.
Example: R code request for NHANES-style survey design
Please draft R code for a survey-weighted logistic regression using NHANES-style design variables. Use placeholder names: OUTCOME, EXPOSURE, AGE, SEX, RACE_ETHNICITY, INCOME, WTMEC2YR, SDMVSTRA, and SDMVPSU. Include comments explaining how to replace the placeholders. Also include a reminder to verify the correct weight for the variables used and whether survey cycles need to be combined.
Example: Stata code request
Please draft Stata code for a survey-weighted regression using placeholder names. Use OUTCOME as the outcome, EXPOSURE as the main predictor, AGE, SEX, and EDUCATION as covariates, WEIGHT as the sampling weight, STRATA as the strata variable, and PSU as the primary sampling unit. Include svyset syntax and comments explaining what the user should verify before running the code.
Example: SAS code request
Please draft SAS code for a survey-weighted logistic regression using placeholder variable names. Use PROC SURVEYLOGISTIC. Include placeholders for the outcome, exposure, covariates, strata, cluster/PSU, and weight. Add comments explaining what the user should verify in the documentation before running the code.
Example: SPSS code request
Please draft SPSS syntax for a regression analysis using placeholder variable names. Include comments that explain what should be replaced, how missing values should be checked, and what output should be reviewed before interpretation.
Example: Python code request
Please draft Python code using pandas and statsmodels for a regression analysis with placeholder variable names. Include comments explaining the data checks, model specification, and output interpretation. Do not assume the data are already clean.
Code review checklist
Before using AI-generated code, users should check:
- Does the code match the intended analysis?
- Are all variable names correct?
- Are placeholder names replaced correctly?
- Are missing values handled appropriately?
- Are survey weights, strata, PSUs, clusters, or repeated measures handled correctly?
- Does the code use the correct analytic sample?
- Does the code create or recode variables correctly?
- Does the model match the outcome type?
- Does the output support the interpretation?
- Has the code been tested on simulated or approved data?
- Has the final code been saved and documented?
Important reminder
StatWiseAI can help draft code, but it cannot guarantee that the code is correct for a specific dataset or research question. Users should review code carefully and consult a statistician or data analyst when needed.
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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