refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-research-methodology
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title: Research Methodology
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Research Methods, Empirical Research, Scientific Method]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [research, science, methodology, statistics, ml-research]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: scientific-method
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---
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# Research Methodology
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## 매 한 줄
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> **"매 a result without a method is folklore."**. 매 Popper 의 falsifiability, Fisher 의 experimental design, Tukey 의 EDA 의 합주 — 매 systematic procedures for generating defensible knowledge claims. 매 2026 ML/AI research 의 reproducibility crisis (60%+ papers fail replication) 으로 매 method rigor 가 더 중요.
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## 매 핵심
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### 매 spectrum
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- **Quantitative**: 매 numeric, statistical inference, causal claims.
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- **Qualitative**: 매 thematic, interpretivist, descriptive depth.
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- **Mixed-methods**: 매 sequential or concurrent triangulation.
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### 매 designs
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- **Experimental**: 매 RCT — random assignment to treatment/control.
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- **Quasi-experimental**: 매 diff-in-diff, regression discontinuity, synthetic control.
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- **Observational**: 매 cross-sectional, longitudinal, case-control.
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- **Computational**: 매 ablation, benchmark, simulation, A/B.
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### 매 quality criteria
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- **Validity**: 매 construct, internal, external, statistical conclusion.
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- **Reliability**: 매 repeatable measurement.
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- **Reproducibility**: 매 same data + code → same result.
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- **Replicability**: 매 new data, same protocol → consistent result.
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### 매 응용
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1. ML paper: 매 ablation table + seed-variance + held-out test set.
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2. Product A/B: 매 power analysis → sample size → MDE.
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3. UX study: 매 mixed-method (interview + log analytics).
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4. AI safety eval: 매 capability + propensity + control evaluations.
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## 💻 패턴
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### Pattern 1: Power analysis before experiment
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```python
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from statsmodels.stats.power import NormalIndPower
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analysis = NormalIndPower()
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n = analysis.solve_power(effect_size=0.2, alpha=0.05, power=0.8, ratio=1.0)
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print(f"매 minimum sample per arm: {int(n)+1}")
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```
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### Pattern 2: Pre-registration template (YAML)
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```yaml
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# 매 preregistration.yaml — 매 commit BEFORE running experiment
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hypothesis: "매 LLM with chain-of-thought scores ≥ 5pp higher on GSM8K vs no-CoT"
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primary_outcome: gsm8k_accuracy
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n_per_arm: 1000
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conditions: [no_cot, cot]
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analysis: paired_t_test
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exclusion_criteria: ["api_error", "max_tokens_truncated"]
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seeds: [0, 1, 2, 3, 4]
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```
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### Pattern 3: Reproducible experiment seed control
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```python
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import random, numpy as np, torch, os
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def set_all_seeds(s):
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random.seed(s); np.random.seed(s); torch.manual_seed(s)
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torch.cuda.manual_seed_all(s)
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os.environ["PYTHONHASHSEED"] = str(s)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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```
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### Pattern 4: Ablation table generation
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```python
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import itertools, pandas as pd
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def ablation_runs(components, base_run):
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rows = []
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for subset in itertools.combinations(components, len(components)-1):
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cfg = base_run.copy();
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removed = [c for c in components if c not in subset][0]
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cfg["removed"] = removed
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cfg["score"] = run(cfg)
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rows.append(cfg)
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return pd.DataFrame(rows)
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```
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### Pattern 5: Confidence interval reporting (not just p-values)
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```python
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import scipy.stats as st
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def ci(scores, alpha=0.05):
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m = np.mean(scores); s = np.std(scores, ddof=1); n = len(scores)
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h = s / np.sqrt(n) * st.t.ppf(1 - alpha/2, n-1)
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return m, m-h, m+h
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# 매 always report (mean, lo, hi) — 매 not just "significant"
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```
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### Pattern 6: Qualitative coding (thematic analysis)
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```python
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# 매 inter-rater reliability via Cohen's kappa
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from sklearn.metrics import cohen_kappa_score
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kappa = cohen_kappa_score(coder_a_codes, coder_b_codes)
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assert kappa > 0.7, "매 coding scheme too ambiguous — refine"
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```
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### Pattern 7: A/B with sequential testing (mSPRT)
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```python
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def msprt_decision(treatment, control, theta=0.01):
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"""매 mixture sequential probability ratio test — 매 anytime-valid."""
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# Lindon & Malek 2020 — 매 lets you peek without inflating type-I
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pass # use external lib like `confseq`
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```
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## 매 결정 기준
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| 상황 | Design |
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|---|---|
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| 매 cause-effect claim | RCT or quasi-experimental |
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| 매 description / mapping | Observational + descriptive stats |
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| 매 user "why" | Qualitative interview + thematic |
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| 매 ML model claim | Ablation + multiple seeds + held-out |
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| 매 product feature decision | A/B with power analysis + pre-reg |
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| 매 emerging behavior | Mixed-methods |
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**기본값**: 매 pre-register + multiple seeds + report CIs + share code & data.
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## 🔗 Graph
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- 부모: [[Statistics]]
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- 변형: [[Causal Inference]]
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## 🤖 LLM 활용
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**언제**: 매 designing experiments, 매 reviewing methodology of papers, 매 drafting pre-registrations.
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**언제 X**: 매 producing fake citations / fabricating data — 매 catastrophic ethics violation.
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## ❌ 안티패턴
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- **HARKing** (Hypothesizing After Results Known): 매 makes p-values meaningless.
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- **p-hacking**: 매 trying many tests until significant.
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- **Single seed reporting**: 매 ML papers — 매 noise dressed as signal.
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- **Overfitting to test set**: 매 multi-stage benchmarks → 매 leakage.
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- **No pre-registration**: 매 invites unconscious bias.
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## 🧪 검증 / 중복
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- Verified (Popper 1959, Fisher 1935, Open Science Framework, Pineau et al. 2021 ML reproducibility checklist).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — design spectrum + ML reproducibility focus |
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