Classification organizes data into logical groups. Types: dichotomous (two categories), polychotomous (multiple categories), hierarchical (nested levels). Key concepts: mutually exclusive categories, exhaustive coverage of all data. Dichotomous example: yes/no, male/female. Polychotomous example: age groups, product categories. Hierarchical example: continents > countries > regions. Common traps: overlapping categories, missing categories, inconsistent classification. Exam tips: ensure clarity in category definitions, check completeness. Time-saving: use existing standard classifications when applicable. Applications: inventory management, quality assessment, demographic analysis. Creating classification system: define purpose, identify characteristics, establish rules. Consistency crucial: same rules applied throughout dataset. Practice developing classification schemes. Clear classification improves data quality and analysis. Master before attempting advanced grouping techniques.