reference-class-forecasting
from lyndonkl/claude
Agents, skills and anything else to use with claude
npx skills add https://github.com/lyndonkl/claude --skill reference-class-forecastingSKILL.md
Reference Class Forecasting
Table of Contents
- What is Reference Class Forecasting?
- When to Use This Skill
- Interactive Menu
- Quick Reference
- Resource Files
What is Reference Class Forecasting?
Reference class forecasting is the practice of anchoring predictions in historical reality by identifying a class of similar past events and using their statistical frequency as a starting point. This is the "Outside View" - looking at what usually happens to things like this, before getting distracted by the specific details of "this case."
Core Principle: Assume this event is average until you have specific evidence proving otherwise.
Why It Matters:
- Defeats "inside view" bias (thinking your case is unique)
- Prevents base rate neglect (ignoring statistical baselines)
- Provides objective anchor before subjective analysis
- Forces humility and statistical thinking
When to Use This Skill
Use this skill when:
- Starting any forecast - Establish base rate FIRST
- Someone says "this time is different" - Test if it really is
- Making predictions about success/failure - Find historical frequencies
- Evaluating startup/project outcomes - Anchor in class statistics
- Challenged by confident predictions - Ground in reality
- Before detailed analysis - Get outside view baseline
Do NOT use when:
- Event has literally never happened (novel situation)
- Working with deterministic physical laws
- Pure chaos with no patterns
Interactive Menu
What would you like to do?
Core Workflows
1. Find My Base Rate - Identify reference class and get statistical baseline
- Guided process to select correct reference class
- Search strategies for finding historical frequencies
- Validation that you have the right anchor
2. Test "This Time Is Different" - Challenge uniqueness claims
- Reversal test for uniqueness bias
- Similarity matching framework
- Burden of proof calculator
3. Calculate Funnel Base Rates - Multi-stage probability chains
- When no single base rate exists
- Sequential probability modeling
- Product rule for compound events
4. Validate My Reference Class - Ensure you chose the right comparison set
- Too broad vs too narrow test
- Homogeneity check
- Sample size evaluation
5. Learn the Framework - Deep dive into methodology
6. Exit - Return to main forecasting workflow
1. Find My Base Rate
Let's establish your statistical baseline.
Step 1: What are you forecasting?
Tell me the specific event or outcome you're predicting.
Example prompts:
- "Will this startup succeed?"
- "Will this bill pass Congress?"
- "Will this project launch on time?"
Step 2: Identify the Reference Class
I'll help you identify what bucket this belongs to.
Framework:
- Too broad: "All companies" → meaningless
- Just right: "Seed-stage B2B SaaS startups in fintech"
- Too narrow: "Companies founded by people named Steve in 2024" → no data
Key Questions:
- What type of entity is this? (company, bill, project, person, etc.)
- What stage/size/category?
- What industry/domain?
- What time period is relevant?
I'll work with you to refine this until we have a specific, searchable class.
Step 3: Search for Historical Data
I'll help you find the base rate using:
- Web search for published statistics
- Academic studies on success rates
- Government/industry reports
- Proxy metrics if direct data unavailable
Search Strategy:
"historical success rate of [reference class]"
"[reference class] failure statistics"
"[reference class] survival rate"
"what percentage of [reference class]"
Step 4: Set Your Anchor
Once we find the base rate, that becomes your starting probability.
The Rule:
You are NOT allowed to move from this base rate until you have specific, evidence-based reasons in your "inside view" analysis.
Default anchors if no data found:
- Novel innovation: 10-20% (most innovations fail)
- Established industry: 50% (uncertain)
- Regulated/proven process: 70-80% (systems work)
Next: Return to menu or proceed to inside view analysis.
2. Test "This Time Is Different"
Challenge uniqueness bias.
When someone (including yourself) believes "this case is special," we need to stress-test that belief.
The Uniqueness Audit
Question 1: Similarity Matching
- What are 5 historical cases that are most si
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