experiment-design
Design scientific experiments including sample size calculation, randomization, control groups, blinding, and study protocols. Covers RCTs, quasi-experiments, factorial designs, A/B tests, survey design, and observational studies. Use when user asks to design an experiment, calculate sample size, plan a study, set up controls, or create a research protocol. Triggers on "design experiment", "sample size", "power analysis", "study design", "control group", "randomization", "A/B test", "factorial design", "survey design".
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SKILL.md
Instructions source · Aperçu en lecture seule- name
- experiment-design
- description
- Design scientific experiments including sample size calculation, randomization, control groups, blinding, and study protocols. Covers RCTs, quasi-experiments, factorial designs, A/B tests, survey design, and observational studies. Use when user asks to design an experiment, calculate sample size, plan a study, set up controls, or create a research protocol. Triggers on "design experiment", "sample size", "power analysis", "study design", "control group", "randomization", "A/B test", "factorial design", "survey design".
# Experiment Design
Scientific experiment planning, power analysis, and protocol development.
## Design Selection Guide
| Research Question | Recommended Design |
|---|---|
| Does X cause Y? | RCT (gold standard) |
| Does X cause Y? (can't randomize) | Quasi-experiment, natural experiment |
| How do factors interact? | Factorial design |
| Which version performs better? | A/B test |
| What is the prevalence/association? | Cross-sectional survey |
| How does outcome change over time? | Longitudinal / cohort study |
| What is the lived experience? | Qualitative (interviews, ethnography) |
| Does intervention work in practice? | Pragmatic trial |
## Power Analysis & Sample Size
```python
source /Users/zhangmingda/clawd/.venv/bin/activate
python3 << 'EOF'
from scipy import stats
import numpy as np
# --- Two-sample t-test ---
def sample_size_ttest(effect_size, alpha=0.05, power=0.80):
"""Cohen's d effect sizes: small=0.2, medium=0.5, large=0.8"""
from scipy.stats import norm
z_alpha = norm.ppf(1 - alpha/2)
z_beta = norm.ppf(power)
n = 2 * ((z_alpha + z_beta) / effect_size) ** 2
return int(np.ceil(n))
# --- Chi-square test ---
def sample_size_chi2(effect_size, alpha=0.05, power=0.80, df=1):
"""Cohen's w effect sizes: small=0.1, medium=0.3, large=0.5"""
from scipy.stats import norm, chi2
z_beta = norm.ppf(power)
z_alpha = norm.ppf(1 - alpha)
n = ((z_alpha + z_beta) / effect_size) ** 2
return int(np.ceil(n))
# --- Correlation ---
def sample_size_correlation(r, alpha=0.05, power=0.80):
from scipy.stats import norm
z_alpha = norm.ppf(1 - alpha/2)
z_beta = norm.ppf(power)
z_r = 0.5 * np.log((1+r)/(1-r)) # Fisher's z
n = ((z_alpha + z_beta) / z_r) ** 2 + 3
return int(np.ceil(n))
# Examples
print(f"t-test (d=0.5): n={sample_size_ttest(0.5)} per group")
print(f"t-test (d=0.3): n={sample_size_ttest(0.3)} per group")
print(f"Chi-square (w=0.3): n={sample_size_chi2(0.3)}")
print(f"Correlation (r=0.3): n={sample_size_correlation(0.3)}")
EOF
```
## Key Design Principles
### Controls
- **Positive control**: Known to produce effect (validates method works)
- **Negative control**: Known to produce no effect (validates baseline)
- **Placebo control**: Inert treatment (controls for expectation effects)
- **Active control**: Existing standard treatment (for superiority/non-inferiority)
### Randomization
- **Simple**: Coin flip / random number
- **Block**: Ensures equal groups per block
- **Stratified**: Randomize within strata (age, sex, severity)
- **Cluster**: Randomize groups, not individuals
### Blinding
- **Single-blind**: Participants don't know assignment
- **Double-blind**: Participants and researchers don't know
- **Triple-blind**: Participants, researchers, and analysts don't know
### Bias Mitigation
| Bias | Mitigation |
|------|-----------|
| Selection bias | Random sampling, clear inclusion criteria |
| Allocation bias | Random assignment, concealed allocation |
| Performance bias | Blinding, standardized protocols |
| Detection bias | Blinded outcome assessment |
| Attrition bias | ITT analysis, minimize dropout |
| Reporting bias | Pre-registration, analysis plan |
## Study Protocol Template
```markdown
# Study Protocol: [Title]
## 1. Background & Rationale
## 2. Objectives & Hypotheses
- Primary:
- Secondary:
## 3. Study Design
- Type: [RCT / quasi-experiment / observational / ...]
- Duration:
## 4. Participants
- Population:
- Inclusion criteria:
- Exclusion criteria:
- Sample size: N = [calculated], power = 0.80, α = 0.05
## 5. Intervention / Exposure
## 6. Outcome Measures
- Primary:
- Secondary:
## 7. Randomization & Blinding
## 8. Data Collection Procedures
## 9. Statistical Analysis Plan
- Primary analysis:
- Secondary analyses:
- Handling of missing data:
## 10. Ethical Considerations
- IRB/Ethics approval:
- Informed consent:
- Data privacy:
## 11. Timeline
## 12. Budget
```
## Pre-registration
Recommend pre-registration for confirmatory studies:
- **OSF**: osf.io (general)
- **ClinicalTrials.gov**: clinical trials
- **PROSPERO**: systematic reviews
- **AsPredicted**: aspredicted.org (quick)
## Tips
- Always justify sample size with power analysis
- Pre-register hypotheses and analysis plan
- Plan for 10-20% attrition in sample size calculation
- Document all deviations from protocol
- Consider pilot study for novel methods
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