Data-Driven Investigation Techniques

# Data-Driven Investigation Techniques

The most impactful investigative stories of the past decade have been powered by data analysis — from the Panama Papers to local investigations of police misconduct. AI makes data-driven investigation accessible to reporters who are not data scientists, democratizing a methodology that was once limited to the largest newsrooms.

Hypothesis-Driven Data Investigation

Start every data investigation with a clear hypothesis:

My hypothesis is: [SPECIFIC CLAIM YOU WANT TO TEST]
Example: "The city's building inspection department is inspecting buildings owned by political donors less frequently than others."

To test this hypothesis, I need:
1. REQUIRED DATA: What specific datasets do I need? Where are they publicly available?
2. LINKING STRATEGY: How do I connect these datasets? What fields can I join on?
3. ANALYSIS PLAN: What statistical tests or comparisons would confirm or refute my hypothesis?
4. ALTERNATIVE EXPLANATIONS: What innocent explanations might produce the same data pattern?
5. THRESHOLD: What would the data need to show for this to be a publishable story vs. an inconclusive finding?