2nd/10_Wiki/Topic_General/From_Other/Assumptions-vs-Facts.md

---
id: wiki-2026-0508-assumptions-vs-facts
title: Assumptions vs Facts
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Fact-Assumption Distinction, Premise vs Evidence]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [reasoning, epistemology, decision-making, critical-thinking]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: na
---

# Assumptions vs Facts

## 매 한 줄
> **"매 fact 는 매 verifiable observation, 매 assumption 은 매 unverified premise"**. 매 둘 의 conflation 매 most decision failure 의 root. 매 military intelligence (CIA Tradecraft Primer), 매 software engineering (RFC, design doc), 매 LLM agent reasoning (chain-of-thought 매 assumption 명시) 모두 의 핵심 discipline.

## 매 핵심

### 매 정의
- **Fact**: 매 currently verifiable claim — 매 measurement, 매 reproducible observation, 매 authoritative record.
- **Assumption**: 매 not verified, 매 taken as true 매 reasoning 진행 위해. 매 implicit / explicit.
- **Inference**: 매 fact + assumption → 매 conclusion.

### 매 Verification spectrum
- **Hard fact**: 매 measurement (e.g., latency = 142ms p95).
- **Soft fact**: 매 expert testimony / consensus (e.g., "FDA-approved").
- **Reasonable assumption**: 매 base rate / 매 prior (e.g., "user 매 attention < 10s").
- **Speculative assumption**: 매 untested premise (e.g., "competitor 매 Q4 launch").

### 매 응용
1. **Design doc**: 매 "Assumptions" section 별도 — 매 reviewer 검증.
2. **Intelligence analysis**: 매 ACH (Analysis of Competing Hypotheses).
3. **Postmortem**: 매 implicit assumption 적출 — 매 next-time fact 로 verify.
4. **LLM CoT**: 매 reasoning chain 에서 매 assumption 의 explicit tag.

## 💻 패턴

### Pattern 1: 매 Design doc template
```markdown
## Facts
- 매 current p95 latency: 240ms (verified via 매 grafana 2026-05-09).
- 매 user count: 1.2M MAU (analytics dashboard).

## Assumptions
- [A1] 매 traffic grow 30% YoY (prior: 2024-2025 trend).
- [A2] 매 redis cluster 매 horizontal scale 가능 (vendor docs, untested at our scale).

## Inferences
- A1 + Facts → 매 Q4 capacity = 1.56M MAU.
- A2 + Facts → 매 cache layer 매 bottleneck 의 X.

## Validation plan
- A1: 매 monthly reforecast.
- A2: 매 Q3 load-test 8x current.
```

### Pattern 2: 매 ACH (Analysis of Competing Hypotheses)
```python
import numpy as np
hypotheses = ["H1: 매 supply shock", "H2: 매 demand drop", "H3: 매 competitor"]
evidence   = ["E1: price up", "E2: query down", "E3: rival ad spike"]
# 매 매 evidence × hypothesis: consistent (+1), inconsistent (-1), N/A (0)
M = np.array([
    # E1, E2, E3
    [+1,  0,  0],   # H1
    [-1, +1,  0],   # H2
    [ 0, +1, +1],   # H3
])
scores = M.sum(axis=1)
for h, s in zip(hypotheses, scores):
    print(h, s)
# 매 lowest disconfirmed = 매 most likely (CIA tradecraft logic)
```

### Pattern 3: 매 Assumption tagging in CoT
```python
def reason_with_tags(query: str) -> str:
    return llm(f"""
Answer step by step. For every claim:
- Tag [FACT: source] if verifiable.
- Tag [ASSUMP: confidence 0-1] if untested.
- Tag [INFER] if derived.

Q: {query}
""")
```

### Pattern 4: 매 Premortem (assumption stress-test)
```markdown
Imagine the project failed in 6 months. List the 5 most likely
failed assumptions. For each, design a 2-week experiment to test
it now.
```

### Pattern 5: Confidence score 매 calibration
```python
predictions = []  # list of (claim, confidence, actual_outcome)
brier = sum((c - a)**2 for _, c, a in predictions) / len(predictions)
print(f"Brier score: {brier:.3f}")  # 매 lower = better calibration
```

## 매 결정 기준
| 상황 | Treat as |
|---|---|
| 매 metric in current dashboard | Fact (with date) |
| 매 vendor capability claim | Soft fact, 매 verify if critical |
| 매 future user behavior | Assumption — 매 explicit |
| 매 "everyone knows" | 매 strong assumption — 매 challenge |
| 매 LLM output | Assumption until cross-checked |

**기본값**: 매 reasoning 시작 시 매 explicit "Facts" / "Assumptions" 분리. 매 implicit assumption 의 surface — 매 brittle.

## 🔗 Graph
- 부모: [[Belief-Revision]] · [[Bayesian-Updating]]
- 변형: [[Bayes-Theorem]] · [[Hypostatic-Abstraction]]
- 응용: [[Problem Solving Process]] · [[Process_Reflection_Template]]
- Adjacent: [[Big-Picture]] · [[Outside-Thinking]] · [[Anticipation]]

## 🤖 LLM 활용
**언제**: 매 agent design — 매 [FACT]/[ASSUMP] tagging 매 hallucination detection 도움. 매 reasoning trace audit.
**언제 X**: 매 creative ideation — 매 over-tagging 매 flow 방해.

## ❌ 안티패턴
- **Implicit assumption**: 매 unmentioned premise — 매 reviewer 못 catch.
- **Fact inflation**: 매 weak evidence 의 hard fact 처럼 표현.
- **Confidence theater**: 매 "obviously" / "clearly" — 매 hidden assumption marker.
- **Single-source fact**: 매 1 source = 매 still soft. 매 triangulate.
- **Stale fact**: 매 6개월 전 metric — 매 currently fact 인지 재검증.

## 🧪 검증 / 중복
- Verified (CIA Tradecraft Primer 2009, Heuer *Psychology of Intelligence Analysis*, Tetlock *Superforecasting*).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — ACH + 매 design-doc pattern + LLM CoT tagging |