Abstract

Modern science excels at pattern recognition but frequently confuses statistical significance with truth. The precision-scope trade-off forces researchers into a dilemma: precise but impractical studies or broad but causally weak ones. As data scales exponentially, spurious correlations proliferate, exacerbating the reproducibility crisis. To restore credibility, science must move beyond statistical detection and adopt frameworks that prioritize coherence and causal structure.