Data anomalies: In some poorly designed tables, changes to data cause undesirable side effects. What are these consequences commonly called in normalization theory?

Introduction / Context:
Normalization aims to reduce redundancy and prevent problematic side effects when inserting, updating, or deleting data. Recognizing anomalies helps justify decomposition into higher normal forms.

Given Data / Assumptions:

• Anomalies arise from redundancy and inappropriate attribute grouping.
• Types include insertion, update, and deletion anomalies.
• We assume standard relational definitions.

Concept / Approach:
“Modification anomalies” is the umbrella term for unwanted effects caused by data modifications in non-normalized designs. They reflect design flaws that normalization (1NF, 2NF, 3NF, BCNF) tries to address.

Step-by-Step Solution:

Verification / Alternative check:
Normalization literature uses the term “modification anomalies” extensively as the motivation for decomposing relations.

Why Other Options Are Wrong:
Referential integrity constraints: Rules that prevent orphaned references, not the anomalies themselves.
Normal forms: Targets/criteria to prevent anomalies.
Transitive dependencies: A specific dependency type that can cause anomalies but is not the name of the consequence.

Common Pitfalls:
Confusing the cause (dependencies) or cure (normal forms) with the symptom (anomalies).

Final Answer:
modification anomalies.