Outlier Detection Methods

Identifying anomalous data points statistically

Full Analysis

Intel Stream Decrypted

Outlier Detection identifies data points that deviate significantly from expected patterns. Methods include: z-score thresholds (>3σ), IQR-based detection, isolation forests, and DBSCAN clustering. For anomaly research, distinguishing genuine anomalies from measurement errors requires combining statistical methods with physics-based validation.

Strategic_Takeaway

"Statistical outliers need physics validation—not all outliers are anomalies."

Universal_Constants

common threshold

3σ (99.7%)

IQR multiplier

1.5