Absurd statistical certainty refers to the presentation of overly precise confidence intervals or performance metrics that suggest a level of accuracy or reliability far beyond what the data, context, or methodology can reasonably support.

⚠️ Key Characteristics
Tiny margins of error (e.g., ±0.06%) in noisy, retrospective, or observational datasets

Overconfident claims based on model-internal cross-validation, without acknowledging real-world variability

Neglect of uncertainty sources: measurement error, data quality, population differences, or model drift

False sense of credibility: used to impress reviewers or readers, not to reflect statistical reality

🔬 In Context
“The model predicted CAUTI with 97.63% accuracy (±0.06% CI).”
➡ This absurd statistical certainty ignores clinical chaos, human variability, and structural confounding. It pretends that healthcare is physics. It isn’t.

💣 Why It's Problematic
Undermines trust in medical AI and research

Encourages misguided confidence in tools not ready for deployment

Often reflects algorithmic vanity, not robust science