====== 📊 Data-Driven Illusion ======

[[Data]]-driven [[illusion]] refers to the misleading impression that a [[study]] is scientifically [[robust]] or clinically [[meaningful]] solely because it uses large [[dataset]]s or complex [[statistical methods]], despite lacking relevant clinical [[context]], biological [[plausibility]], or [[methodological rigor]].

🔎 Key Characteristics

Massive [[sample size]]s that amplify [[statistical significance]] while masking clinical [[irrelevant]].

Sophisticated analytics (e.g., SMR, APC, machine learning) applied to poorly defined or incomplete variables.

Apparent precision that gives undue [[confidence]] to fundamentally flawed or superficial [[conclusion]]s.

Use of registry or administrative data without proper [[validation]], adjustment for confounders, or stratified analysis.

⚠️ Why It Matters

A data-driven illusion can inflate the credibility of findings that are not actionable, not causal, or even not real — undermining evidence-based practice by dressing speculation in statistical clothing.

🧠 Example Usage in Critique:

“This study suffers from a classic data-driven illusion: massive patient numbers and elegant modeling techniques give the appearance of depth, but in reality, it lacks the clinical resolution and methodological controls needed for meaningful interpretation.”