docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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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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