research-methodology

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5	Training data memory	0.10-0.30	Data from model memory (may be outdated)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

1. Search before estimating: Do not guess from training data if the information can be searched
2. Cross-validate: Confirm key data from at least 2 independent sources
3. Label timeliness: Note the time range of data (e.g., "2024 data")
4. Distinguish facts from opinions: Clearly label when quoting

Data Recording Format

Use wanman artifact put to record each research data point:

# Good recording practice
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"}'

# Bad practice (missing source and confidence)
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• Outdated data: Rent and foot traffic data from 2 years ago may have changed significantly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