| name | research-methodology |
| description | Methodology for market research and data collection, ensuring data quality and source traceability |
Research Methodology
Core Principles
Every data point must have a source and confidence score. Estimates are not facts — label them as estimates.
Data Source Hierarchy
| Tier | Source Type | Confidence Range | Examples |
|---|
| 1 | Official public data | 0.85-0.95 | Government statistics, public company filings, industry association reports |
| 2 | Authoritative media | 0.70-0.85 | Major financial media, industry analysis reports |
| 3 | Web search results | 0.50-0.70 | Blogs, forums, review sites |
| 4 | Model inference/estimates | 0.20-0.50 | Extrapolations from known data, analogical estimates |
| 5 | Training data memory | 0.10-0.30 | Data from model memory (may be outdated) |
Search Strategy
- Search before estimating: Do not guess from training data if the information can be searched
- Cross-validate: Confirm key data from at least 2 independent sources
- Label timeliness: Note the time range of data (e.g., "2024 data")
- Distinguish facts from opinions: Clearly label when quoting
Data Recording Format
Use wanman artifact put to record each research data point:
wanman artifact put --kind market_data --path "competitors/cafe_a/pricing" \
--meta '{"source":"web_search:dianping.com","confidence":0.65,"data_date":"2024-12","avg_price":35,"currency":"CNY","category":"specialty_coffee"}'
wanman artifact put --kind market_data --path "competitors/pricing" \
--meta '{"avg_price":35}'
Common Pitfalls
- Survivorship bias: Successful cases found via search do not represent the full industry picture
- Outdated data: Rent and foot traffic data from 2 years ago may have changed significantly
- Regional differences: Tier-1 city data cannot be directly applied to tier-2 or tier-3 cities
- Currency/unit confusion: Always label currency and measurement units
