Representativeness Bias & Pattern Matching
What is Representativeness?
Representativeness is judging the probability of something by how much it resembles a typical case, ignoring actual statistical realities.
Simple version: "If it looks like a duck, it must be a duck"—even when probability says otherwise.
The Core Error
We substitute a hard probability question with an easier similarity question:
| Statistical Question (Correct) | Similarity Question (Biased) |
|---|---|
| "What's the probability this startup succeeds?" | "Does this startup resemble successful startups?" |
| Requires data, base rates | Pattern matching, stereotypes |
This shortcut leads to systematic errors.
Forms of Representativeness
Stereotyping
"This company has a charismatic CEO like Steve Jobs → Will be next Apple!"
Error: Most companies with charismatic CEOs fail. Base rate of startup success is ~10%. Similarity doesn't override statistics.
Sample Size Neglect
Error: Expecting small samples to reflect population.
Example: Mutual fund with 2-year track record returns 25%/year.
- Investors think: "Great fund, will continue!"
- Reality: Too small sample. Likely luck, not skill. Need 10+ years minimum.
Law of Small Numbers: Mistakenly believing small samples are representative.
- Friend made money in crypto → "I will too!"
- Company had 3 good quarters → "Always profitable!"
Hot IPO Fallacy
When 2-3 IPOs list at 50%+ gains, investors conclude "All IPOs are great!"
Representativeness Error: Recent successful IPOs seem "representative" of category.
Base Rate Reality: ~70% of IPOs underperform market within 3 years.
Investment Errors
| Situation | Representativeness Thinking | Reality |
|---|---|---|
| Stock on 5-day win streak | "Hot stock, momentum!" | Random variation, no predictive power |
| Tech startup reminds of Google | "Will 10x!" | 90% of startups fail (base rate) |
| Fund manager on 2-year hot streak | "Genius!" | Likely luck in small sample |
| New sector booming | "Next big thing!" | Most new sectors have 80%+ failure rates |
Gambler's Fallacy vs Representativeness
Related but different:
- Gambler's Fallacy: "Red 5 times → Black is due" (expecting reversal)
- Representativeness: "5 reds doesn't represent true 50/50 odds, so black must come soon"
Both ignore independence and statistics!
Real Examples
Burger King IPO (India, 2020):
- Recent restaurant IPO success (Jubilant FoodWorks gained 3x)
- BK seemed "representative" of successful food IPOs
- Oversubscribed 156x!
- Reality: Each IPO is different. BK specific fundamentals matter more than category resemblance
- Initial gains followed by underperformance
"Next Tesla" Syndrome:
Whenever EV startup IPOs:
- "Young company, charismatic founder, EV sector → Next Tesla!"
- Representativeness makes it seem probable
- Base rate: Of 50+ EV startups globally, only Tesla succeeded. That's 2% success rate.
Protection Strategies
Always Ask Base Rates
Before pattern matching:
- "What percentage of companies like this succeed?"
- "What's the typical outcome for this category?"
Example: Before investing in IPO because it "seems like winners":
- Check: What % of IPOs outperform after 3 years? (~30%)
- Adjust expectations downward
Demand Large Samples
Don't trust:
- 1-2 year fund track records
- 3-4 good quarters
- Single successful product
Require:
- 10+ years for fund managers
- Multiple economic cycles
- Sustained performance across conditions
Avoid Stereo
typing
"Looks like successful company" ≠ "Will be successful"
Check fundamentals, not resemblance.
Use Statistical Thinking
Replace: "Does this remind me of winners?"
With: "What are the actual odds based on data?"
Key Insights
- Representativeness judges probability by similarity, ignoring base rates
- Law of small numbers: Expect small samples to be representative (they're not)
- Investment errors: Hot streaks, IPO frenzies, "next big thing" hype
- Protection: Check base rates, demand large samples, use statistics not stereotypes
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