Which statement best contrasts hypothesis-driven and data-driven analysis?

• A. Hypothesis-driven analysis starts from patterns.
• B. Hypothesis-driven analysis starts from a testable question; data-driven analysis starts from patterns and lets conclusions emerge. Use hypothesis-driven for decision points; data-driven for anomaly detection.
• C. Data-driven analysis ignores hypotheses.
• D. Hypothesis-driven relies on OSINT.

Answer: (D)

The main idea here is how the starting point shapes the analysis workflow. Hypothesis-driven analysis begins with a testable question or hypothesis and then designs data collection and tests to confirm or refute it. This approach uses predefined criteria for what counts as evidence and aims to reach a decision point by evaluating whether the data support the hypothesis.

Data-driven analysis, by contrast, starts with the data itself—exploring patterns, anomalies, and correlations and letting insights emerge from what the data reveal. It’s particularly useful for discovering unexpected developments or problems and can lead to new hypotheses after initial exploration, rather than enforcing a prior theory from the start.

OSINT can be part of either approach, but it does not define hypothesis-driven analysis. The distinguishing feature is the starting point: a testable question versus patterns-driven exploration.