Statistical Assumption Checker AI Prompt

Check the statistical assumptions underlying my planned analyses and recommend how to handle any violations.

Planned analyses: {{analyses}}
Data characteristics: {{data_description}}

For each planned analysis, check the following assumptions:

1. Linear regression assumptions:
   - Linearity: is the relationship between predictors and outcome linear? Check: scatter plot, residual vs fitted plot. Violation: use polynomial terms, transformations, or GAM.
   - Independence: are observations independent? Check: study design. Violation: use clustered standard errors, mixed models, or GEE.
   - Homoscedasticity (constant variance): does residual variance remain constant across fitted values? Check: scale-location plot. Violation: use robust standard errors (HC3) or transform the outcome.
   - Normality of residuals: are residuals approximately normally distributed? Check: Q-Q plot, Shapiro-Wilk (for small n). Note: normality of RESIDUALS is required, not of the outcome itself. Violation: with n > 30, Central Limit Theorem generally protects. For small n: use robust or nonparametric methods.
   - No perfect multicollinearity: Check: VIF. VIF > 10 is problematic. Violation: drop or combine predictors, use ridge regression.

2. ANOVA assumptions:
   - Normality of group distributions (robust with large n)
   - Homogeneity of variance: Levene's test. Violation: Welch's ANOVA does not require equal variances — use by default.
   - Independence: same as above.

3. Chi-squared test assumptions:
   - Expected cell frequencies ≥ 5 in all cells. Violation: use Fisher's exact test, combine categories, or use exact tests.
   - Independence of observations.

4. Logistic regression assumptions:
   - No perfect separation: if a predictor perfectly predicts the outcome, estimates become unstable. Check: examine cross-tabs of predictors with outcome.
   - Linearity of log-odds: continuous predictors should have a linear relationship with the log-odds. Check: Box-Tidwell transformation test.
   - No influential outliers: Check: Cook's distance, leverage statistics.

5. For each violated assumption:
   - State the likely impact on results (conservative? anti-conservative? biased estimates?)
   - Provide the specific code or procedure to implement the appropriate remedy
   - Explain how to report the assumption check and its handling in the paper

Return: assumption checklist per analysis, violation assessment, remedies with implementation guidance, and reporting language.