Full Statistical Analysis Chain AI Prompt

Step 1: Research question and estimand - state the precise research question in one sentence. Define the estimand: the specific population parameter you are trying to estimate or test. Specify: the target population, the exposure or treatment, the outcome, and the comparison (vs what baseline or control?).
Step 2: Study design assessment - evaluate the study design: was randomization used? If observational, what is the primary confounding threat? Draw the causal DAG and identify the minimal sufficient adjustment set.
Step 3: Data quality check - assess the data for: missing values (pattern and % per variable), outliers (flag observations > 3 SD from mean), distributional assumptions (normality, homoscedasticity), and any data entry anomalies.
Step 4: Descriptive statistics - produce a Table 1: describe all variables by group. For continuous variables: mean (SD) or median [IQR] based on distribution. For categorical: count (%). Test baseline differences if a two-group comparison.
Step 5: Primary analysis - select and run the primary statistical test. Report: test statistic, degrees of freedom, p-value, effect size, and 95% confidence interval. Check all assumptions and note any violations.
Step 6: Secondary and sensitivity analyses - run planned secondary analyses. Conduct a sensitivity analysis: repeat the primary analysis under different assumptions (e.g., complete cases vs imputed, alternative covariate adjustment sets). Assess robustness.
Step 7: Interpretation and reporting - write a plain-language summary of findings. Interpret the effect size in practical terms. Discuss limitations. Specify what the results can and cannot conclude. Produce the statistical methods section text.