===== Interpretative Overconfidence =====

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

==== Common manifestations ====
  * 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

==== Example in clinical research ====
> Claiming that a machine learning model can **prevent disease** simply because it predicts risk with high accuracy on retrospective data.

==== Consequences ====
  * 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.