'Interpretative overconfidence' occurs when researchers express excessive certainty about the meaning or implications of their findings, going beyond what the data objectively support.

• Drawing causal conclusions from correlational or observational data
• Presenting model outputs (e.g., risk scores, AUCs, SHAP values) as clinically actionable without external validation
• Ignoring limitations or uncertainty in measurement, sampling, or context
• Overstating the generalizability or novelty of results

Claiming that a machine learning model can prevent disease simply because it predicts risk with high accuracy on retrospective data.

• Misguides clinical decision-making
• Inflates perceived scientific progress
• Erodes public and professional trust in medical research

'In summary:' interpretative overconfidence distorts the relationship between evidence and conclusion, leading to potentially misleading or unjustified claims.