| name | fact-creation-methodologies |
| description | Create novel facts and insights when existing data is insufficient by designing hypothesis-driven surveys, synthesizing data from multiple sources, and analyzing outliers. Use in business analysis, market research, or strategic consulting when you need to challenge conventional wisdom, generate unexpected truths, or prove/disprove a hypothesis. |
Fact Creation Methodologies
When to Use
Apply these techniques when:
- Existing government statistics or public data are insufficient for analysis
- Common knowledge doesn't provide adequate insights
- You need to prove or disprove a specific hypothesis
- Conventional wisdom requires challenging with new perspectives
- Initial analysis yields only obvious or expected results
Prerequisites
- Defined problem statement
- Initial hypothesis to test or validate
Methodology 1: Hypothesis-Driven Questionnaire Design
Design surveys specifically to prove a hypothesis rather than merely gathering descriptive data.
Steps:
- Frame questions to test your hypothesis directly
- Structure options and phrasing to reveal unexpected truths
- Avoid questions that merely confirm common sense or obvious trends
- Be conscious of how question phrasing influences results (e.g., customer satisfaction scales, rating options)
Goal: Discover "unexpected truths" or "unreasonable truths" that challenge assumptions, not just data that confirms what is already known.
Methodology 2: Cross-Source Data Synthesis
Combine data from different origins to create novel insights that don't exist in any single source.
Steps:
- Identify data sets with overlapping variables but different contexts
- Overlay or intersect data to reveal new relationships
- Change premises or viewpoints to derive new answers from existing numbers
- Look for analogous patterns across different domains or time periods
Example Approach: Simulate potential outcomes by applying patterns from one context to another (e.g., using historical recovery data from one country to model another's projections).
Methodology 3: Exception Analysis
Focus on outliers and deviations rather than averages or broad trends.
Steps:
- Identify data points that deviate significantly from the mean
- Analyze areas where trends contradict the overall pattern
- Investigate causes behind these exceptions to find hidden drivers
- Use exception findings to shift the direction of analysis or discussion
Goal: Reveal "blind points" in the data that average-based analysis obscures.
Output
A newly created fact or insight that:
- Challenges conventional wisdom
- Provides a novel perspective on the problem
- Supports or refutes the initial hypothesis
- Goes beyond summarizing existing known data
Constraints
- Do not rely solely on existing government statistics
- Avoid simply summarizing known data
- Ensure results provide unexpected value, not just confirmation of what's already known
