• What is Reference Class Forecasting?
• When to Use This Skill
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• 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

• 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

• Event has literally never happened (novel situation)
• Working with deterministic physical laws
• Pure chaos with no patterns

• Guided process to select correct reference class
• Search strategies for finding historical frequencies
• Validation that you have the right anchor

• Reversal test for uniqueness bias
• Similarity matching framework
• Burden of proof calculator

• When no single base rate exists
• Sequential probability modeling
• Product rule for compound events

• Too broad vs too narrow test
• Homogeneity check
• Sample size evaluation

• Read Outside View Principles
• Read Reference Class Selection Guide
• Read Common Pitfalls

• "Will this startup succeed?"
• "Will this bill pass Congress?"
• "Will this project launch on time?"

• 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

1. What type of entity is this? (company, bill, project, person, etc.)
2. What stage/size/category?
3. What industry/domain?
4. What time period is relevant?

• Web search for published statistics
• Academic studies on success rates
• Government/industry reports
• Proxy metrics if direct data unavailable

"historical success rate of [reference class]"
"[reference class] failure statistics"
"[reference class] survival rate"
"what percentage of [reference class]"

You are NOT allowed to move from this base rate until you have specific, evidence-based reasons in your "inside view" analysis.

• Novel innovation: 10-20% (most innovations fail)
• Established industry: 50% (uncertain)
• Regulated/proven process: 70-80% (systems work)

• What are 5 historical cases that are most similar to this one?
• For each, what was the outcome?
• How is your case materially different from these?

• If someone claimed a different case was "unique" for the same reasons you're claiming, would you accept it?
• Are you applying special pleading?

|Y - X|

• Gap < 10%: Minimal evidence needed
• Gap 10-30%: Moderate evidence needed (2-3 specific factors)
• Gap > 30%: Extraordinary evidence needed (multiple independent strong signals)

1. Whether "this time is different" is justified
2. How much you can reasonably adjust from the base rate
3. What evidence would be needed to justify larger moves

• No direct statistic exists (e.g., "success rate of X")
• Event requires multiple sequential steps
• Each stage has independent probabilities

1. Stage 1: Bills introduced → Bills that reach committee
   • P(committee | introduced) = ?
2. Stage 2: Bills in committee → Bills that reach floor vote
   • P(floor | committee) = ?
3. Stage 3: Bills voted on → Bills that pass
   • P(pass | floor vote) = ?

P(law) = P(committee) × P(floor) × P(pass)

1. Decompose the event into sequential stages
2. Search for statistics on each stage
3. Multiply probabilities using the product rule
4. Validate the model (are stages truly independent?)

• Startup success: Seed → Series A → Profitability → Exit
• Drug approval: Discovery → Trials → FDA → Market
• Project delivery: Planning → Development → Testing → Launch

• Are the members of this class actually similar enough?
• Is there high variance in outcomes?
• Should you subdivide further?

• Do you have enough historical cases?
• Minimum: 20-30 cases for meaningful statistics
• If N < 20: Widen the class or acknowledge high uncertainty

• Have conditions changed since the historical data?
• Are there structural differences (regulation, technology, market)?
• Time decay: Data from >10 years ago may be stale

• Class has 20+ historical examples
• Members are reasonably homogeneous
• Data is from relevant time period
• No major structural changes since data collection
• Class is specific enough to be meaningful
• Class is broad enough to have data

• Statistical thinking vs narrative thinking
• Why the outside view beats experts
• Kahneman's planning fallacy research
• When outside view fails

• Systematic method for choosing comparison sets
• Balancing specificity vs data availability
• Similarity metrics and matching
• Edge cases and judgment calls

• Base rate neglect examples
• "This time is different" bias
• Overfitting to small samples
• Ignoring regression to the mean
• Availability bias in class selection

1. Base Rate First: Establish statistical baseline BEFORE analyzing specifics
2. Assume Average: Treat case as typical until proven otherwise
3. Burden of Proof: Large deviations from base rate require strong evidence
4. Class Precision: Reference class should be specific but data-rich
5. No Narratives: Resist compelling stories; trust frequencies

Find what usually happens to things like this, start there, and only move with evidence.

• Before: Use

  estimation-fermi

  if you need to calculate base rate from components
• After: Use

  bayesian-reasoning-calibration

  to update from base rate with new evidence
• Companion: Use

  scout-mindset-bias-check

  to validate you're not cherry-picking the reference class

• outside-view-principles.md - Theory and research
• reference-class-selection.md - Systematic selection method
• common-pitfalls.md - What to avoid