---
id: wiki-2026-0508-research-methodology
title: Research Methodology
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Research Methods, Empirical Research, Scientific Method]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [research, science, methodology, statistics, ml-research]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: scientific-method
---

# Research Methodology

## 매 한 줄
> **"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.

## 매 핵심

### 매 spectrum
- **Quantitative**: 매 numeric, statistical inference, causal claims.
- **Qualitative**: 매 thematic, interpretivist, descriptive depth.
- **Mixed-methods**: 매 sequential or concurrent triangulation.

### 매 designs
- **Experimental**: 매 RCT — random assignment to treatment/control.
- **Quasi-experimental**: 매 diff-in-diff, regression discontinuity, synthetic control.
- **Observational**: 매 cross-sectional, longitudinal, case-control.
- **Computational**: 매 ablation, benchmark, simulation, A/B.

### 매 quality criteria
- **Validity**: 매 construct, internal, external, statistical conclusion.
- **Reliability**: 매 repeatable measurement.
- **Reproducibility**: 매 same data + code → same result.
- **Replicability**: 매 new data, same protocol → consistent result.

### 매 응용
1. ML paper: 매 ablation table + seed-variance + held-out test set.
2. Product A/B: 매 power analysis → sample size → MDE.
3. UX study: 매 mixed-method (interview + log analytics).
4. AI safety eval: 매 capability + propensity + control evaluations.

## 💻 패턴

### Pattern 1: Power analysis before experiment
```python
from statsmodels.stats.power import NormalIndPower

analysis = NormalIndPower()
n = analysis.solve_power(effect_size=0.2, alpha=0.05, power=0.8, ratio=1.0)
print(f"매 minimum sample per arm: {int(n)+1}")
```

### Pattern 2: Pre-registration template (YAML)
```yaml
# 매 preregistration.yaml — 매 commit BEFORE running experiment
hypothesis: "매 LLM with chain-of-thought scores ≥ 5pp higher on GSM8K vs no-CoT"
primary_outcome: gsm8k_accuracy
n_per_arm: 1000
conditions: [no_cot, cot]
analysis: paired_t_test
exclusion_criteria: ["api_error", "max_tokens_truncated"]
seeds: [0, 1, 2, 3, 4]
```

### Pattern 3: Reproducible experiment seed control
```python
import random, numpy as np, torch, os

def set_all_seeds(s):
    random.seed(s); np.random.seed(s); torch.manual_seed(s)
    torch.cuda.manual_seed_all(s)
    os.environ["PYTHONHASHSEED"] = str(s)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
```

### Pattern 4: Ablation table generation
```python
import itertools, pandas as pd

def ablation_runs(components, base_run):
    rows = []
    for subset in itertools.combinations(components, len(components)-1):
        cfg = base_run.copy(); 
        removed = [c for c in components if c not in subset][0]
        cfg["removed"] = removed
        cfg["score"] = run(cfg)
        rows.append(cfg)
    return pd.DataFrame(rows)
```

### Pattern 5: Confidence interval reporting (not just p-values)
```python
import scipy.stats as st
def ci(scores, alpha=0.05):
    m = np.mean(scores); s = np.std(scores, ddof=1); n = len(scores)
    h = s / np.sqrt(n) * st.t.ppf(1 - alpha/2, n-1)
    return m, m-h, m+h
# 매 always report (mean, lo, hi) — 매 not just "significant"
```

### Pattern 6: Qualitative coding (thematic analysis)
```python
# 매 inter-rater reliability via Cohen's kappa
from sklearn.metrics import cohen_kappa_score
kappa = cohen_kappa_score(coder_a_codes, coder_b_codes)
assert kappa > 0.7, "매 coding scheme too ambiguous — refine"
```

### Pattern 7: A/B with sequential testing (mSPRT)
```python
def msprt_decision(treatment, control, theta=0.01):
    """매 mixture sequential probability ratio test — 매 anytime-valid."""
    # Lindon & Malek 2020 — 매 lets you peek without inflating type-I
    pass  # use external lib like `confseq`
```

## 매 결정 기준
| 상황 | Design |
|---|---|
| 매 cause-effect claim | RCT or quasi-experimental |
| 매 description / mapping | Observational + descriptive stats |
| 매 user "why" | Qualitative interview + thematic |
| 매 ML model claim | Ablation + multiple seeds + held-out |
| 매 product feature decision | A/B with power analysis + pre-reg |
| 매 emerging behavior | Mixed-methods |

**기본값**: 매 pre-register + multiple seeds + report CIs + share code & data.

## 🔗 Graph
- 부모: [[Statistics]]
- 변형: [[Causal Inference]]

## 🤖 LLM 활용
**언제**: 매 designing experiments, 매 reviewing methodology of papers, 매 drafting pre-registrations.
**언제 X**: 매 producing fake citations / fabricating data — 매 catastrophic ethics violation.

## ❌ 안티패턴
- **HARKing** (Hypothesizing After Results Known): 매 makes p-values meaningless.
- **p-hacking**: 매 trying many tests until significant.
- **Single seed reporting**: 매 ML papers — 매 noise dressed as signal.
- **Overfitting to test set**: 매 multi-stage benchmarks → 매 leakage.
- **No pre-registration**: 매 invites unconscious bias.

## 🧪 검증 / 중복
- Verified (Popper 1959, Fisher 1935, Open Science Framework, Pineau et al. 2021 ML reproducibility checklist).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — design spectrum + ML reproducibility focus |