Statistical Test Selector AI Prompt

Help me choose the correct statistical test for my research question and data.

Research question: {{research_question}}
Outcome variable type: {{outcome_type}} (continuous, binary, count, ordinal, time-to-event)
Predictor/grouping variable: {{predictor}} (categorical with N groups, continuous)
Study design: {{design}} (between-subjects, within-subjects, mixed, longitudinal)
Sample size: {{n}}

1. Decision tree for test selection:

   Comparing means or distributions:
   - 2 independent groups, continuous outcome → Independent samples t-test (if normal) or Mann-Whitney U (if not)
   - 2 related groups / repeated measures, continuous outcome → Paired t-test (if normal) or Wilcoxon signed-rank
   - 3+ independent groups, continuous outcome → One-way ANOVA (if normal) or Kruskal-Wallis
   - 3+ groups with covariates → ANCOVA
   - Repeated measures with 3+ time points → Repeated measures ANOVA or linear mixed model

   Associations:
   - Two continuous variables → Pearson correlation (if both normal) or Spearman (if not)
   - Continuous outcome, multiple predictors → Multiple linear regression
   - Binary outcome → Logistic regression
   - Count outcome → Poisson regression (or negative binomial if overdispersed)
   - Time-to-event outcome → Cox proportional hazards regression
   - Ordinal outcome → Ordinal logistic regression

   Categorical associations:
   - Two categorical variables → Chi-squared test (if expected cell counts ≥ 5) or Fisher's exact (if small cells)

2. Check the assumptions of the recommended test:
   - State each assumption
   - Explain how to test whether each assumption is met
   - For each violated assumption: state the appropriate alternative or robust version

3. The model-based alternative:
   For most research questions, a regression model is preferable to a simple test because:
   - It accommodates covariates and confounders
   - It provides effect size estimates with confidence intervals
   - It handles unbalanced designs gracefully
   - It generalizes to more complex designs
   Recommend the regression equivalent of the chosen test.

4. Multiple outcomes:
   If testing more than one outcome, explain:
   - The multiple comparisons problem
   - Whether a family-wise correction (Bonferroni, Holm) or false discovery rate approach is appropriate
   - How to designate a primary outcome to preserve Type I error rate

Return: recommended test and its regression equivalent, assumptions and how to test them, multiple comparisons guidance.