'Methodological overreach' refers to the inappropriate extension of a study’s methodology beyond what the data or design can reliably support.

It occurs when researchers:

• Make claims that exceed the limitations of the study design (e.g., causal claims from observational data).
• Apply complex statistical or machine learning tools without adequate justification or validation.
• Generalize findings to populations or clinical settings not represented in the data.
• Use sophisticated methods (e.g., SHAP, deep learning) to add perceived value without improving scientific rigor.

• Using a cross-sectional dataset to build a tool for longitudinal prediction.
• Claiming preventive or therapeutic impact from an observational model.
• Presenting model performance metrics (like AUC) as proof of clinical utility.

• Misleading conclusions
• False sense of confidence in tools or interventions
• Poor translation into real-world clinical practice

'In summary:' methodological overreach undermines scientific integrity by overstating what a study can truly demonstrate.