Decision representations: what is the key difference between decision tables and decision trees?

Introduction / Context:
Decision tables and decision trees are complementary ways to specify complex conditional logic. Both aim to make rules explicit and unambiguous.

Given Data / Assumptions:

• Decision tables list conditions/actions in a matrix, enumerating combinations.
• Decision trees depict conditions as internal nodes and actions as leaves.
• Both communicate logic to analysts, developers, and testers.

Concept / Approach:
The essential distinction is visual and structural. Tables are compact for many conditions with mutually exclusive rules; trees are intuitive for hierarchical “if-then-else” branching and for explaining paths.

Step-by-Step Solution:
1) Identify purpose overlap: both express logic.2) Identify structural difference: matrix vs graph.3) Conclude that representation form is the key difference.

Verification / Alternative check:
Modeling guides often recommend converting between the two to check completeness/consistency.

Why Other Options Are Wrong:
(A) End-user value is similar—clarity of rules. (C) Both show logic; “process flow” wording is misleading for trees, which still encode logic paths.

Common Pitfalls:
Assuming one is inherently superior; the choice depends on rule density and audience.

Final Answer:
Form of representation differs (tabular matrix vs branching graph).