Can AI tools help analyze the statistical assumptions in articles?

Yes, AI tools can assist in analyzing the statistical assumptions underlying analyses presented in academic articles. Advanced AI models, particularly those optimized for technical reasoning like ChatGPT-4, have demonstrated significant capability in identifying, evaluating, and discussing key statistical concepts. These tools can systematically identify common assumptions (e.g., normality, independence, homoscedasticity, linearity) mentioned or implied within the methods section. They can assess whether appropriate diagnostic tests or visualizations (e.g., QQ-plots, residual plots, significance tests) were used and reported to verify these assumptions. Furthermore, AI can flag potential violations discussed in the results or apparent in the analysis type, pointing out methodological limitations if assumptions appear unmet without justification. However, this AI analysis relies entirely on the text and data reported within the article; it cannot inspect raw data directly and lacks the nuanced judgment of experienced statisticians, so its insights should be treated as supportive rather than definitive. This capability adds significant value to the literature review and critical appraisal process. AI can rapidly screen articles, highlighting potential methodological weaknesses related to statistical assumptions that might invalidate results or limit generalizability. This assists researchers in prioritizing articles for deeper scrutiny, identifying trends in methodological rigor within a field, and improving the robustness of subsequent research designs by learning from potential oversights. It enhances efficiency and reduces the risk of overlooking critical assumption checks.