experiment-design

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, ca...
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

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

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((+r)/(-r))  
    n = ((z_alpha + z_beta) / z_r) **  + 
     (np.ceil(n))


()
()
()
()
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

# 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

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If execution fails, report the failure point, summarize what can still be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of experiment-design and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

experiment-design only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.