home / skills / lyndonkl / claude / design-of-experiments
This skill helps you design efficient experiments to identify key factors and optimize performance with minimal runs.
npx playbooks add skill lyndonkl/claude --skill design-of-experimentsReview the files below or copy the command above to add this skill to your agents.
---
name: design-of-experiments
description: Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.
---
# Design of Experiments
## Table of Contents
- [Purpose](#purpose)
- [When to Use](#when-to-use)
- [What Is It?](#what-is-it)
- [Workflow](#workflow)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#quick-reference)
## Purpose
Design of Experiments (DOE) helps you systematically discover how multiple factors affect an outcome while minimizing the number of experimental runs. Instead of testing one variable at a time (inefficient) or guessing randomly (unreliable), DOE uses structured experimental designs to:
- **Screen** many factors to find the critical few
- **Optimize** factor settings to maximize/minimize a response
- **Discover interactions** where factors affect each other
- **Map response surfaces** to understand the full factor space
- **Validate robustness** against noise and environmental variation
## When to Use
Use this skill when:
- **Limited experimental budget**: You have constraints on time, cost, or resources for testing
- **Multiple factors**: 3+ controllable variables that could affect the outcome
- **Interaction suspicion**: Factors may interact (effect of A depends on level of B)
- **Optimization needed**: Finding best settings, not just "better than baseline"
- **Screening required**: Many candidate factors (10+), need to identify vital few
- **Response surface**: Need to map curvature, find peaks/valleys, understand tradeoffs
- **Robust design**: Must work well despite noise factors or environmental variation
- **Process improvement**: Manufacturing, chemical processes, software performance tuning
- **Product development**: Formulations, recipes, configurations with multiple parameters
- **A/B/n testing**: Web/app features with multiple variants and combinations
- **Machine learning**: Hyperparameter tuning for models with many parameters
Trigger phrases: "optimize", "tune parameters", "factorial test", "interaction effects", "response surface", "efficient experiments", "minimize runs", "robustness", "sensitivity analysis"
## What Is It?
Design of Experiments is a statistical framework for planning, executing, and analyzing experiments where you deliberately vary multiple input factors to observe effects on output responses.
**Quick example:**
You're optimizing a web signup flow with 3 factors:
- **Factor A**: Form layout (single-page vs multi-step)
- **Factor B**: CTA button color (blue vs green)
- **Factor C**: Social proof (testimonials vs user count)
**Naive approach**: Test one at a time = 6 runs (2 levels each × 3 factors)
- But you miss interactions! Maybe blue works better for single-page, green for multi-step.
**DOE approach**: 2³ factorial design = 8 runs
- Tests all combinations: (single/blue/testimonials), (single/blue/count), (single/green/testimonials), etc.
- Reveals main effects AND interactions
- Statistical power to detect differences
**Result**: You discover that layout and CTA color interact strongly—multi-step + green outperforms everything, but single-page + blue is close second. Social proof has minimal effect. Make data-driven decision with confidence.
## Workflow
Copy this checklist and track your progress:
```
Design of Experiments Progress:
- [ ] Step 1: Define objectives and constraints
- [ ] Step 2: Identify factors, levels, and responses
- [ ] Step 3: Choose experimental design
- [ ] Step 4: Plan execution details
- [ ] Step 5: Create experiment plan document
- [ ] Step 6: Validate quality
```
**Step 1: Define objectives and constraints**
Clarify the experiment goal (screening vs optimization), response metric(s), experimental budget (max runs), time/cost constraints, and success criteria. See [Common Patterns](#common-patterns) for typical objectives.
**Step 2: Identify factors, levels, and responses**
List all candidate factors (controllable inputs), specify levels for each factor (low/high or discrete values), categorize factors (control vs noise), and define response variables (measurable outputs). For screening many factors (8+), see [resources/methodology.md](resources/methodology.md#screening-designs) for Plackett-Burman and fractional factorial approaches.
**Step 3: Choose experimental design**
Based on objective and constraints:
- **For screening 5+ factors with limited runs** → Use [resources/methodology.md](resources/methodology.md#screening-designs) for fractional factorial or Plackett-Burman
- **For optimizing 2-5 factors** → Use [resources/template.md](resources/template.md#factorial-designs) for full or fractional factorial
- **For response surface mapping** → Use [resources/methodology.md](resources/methodology.md#response-surface-methodology) for central composite or Box-Behnken
- **For robust design against noise** → Use [resources/methodology.md](resources/methodology.md#taguchi-methods) for parameter vs noise factor arrays
**Step 4: Plan execution details**
Specify randomization order (eliminate time trends), blocking strategy (control nuisance variables), replication plan (estimate error), sample size justification (power analysis), and measurement protocols. See [Guardrails](#guardrails) for critical requirements.
**Step 5: Create experiment plan document**
Create `design-of-experiments.md` with sections: objective, factors table, design matrix (run order with factor settings), response variables, execution protocol, and analysis plan. Use [resources/template.md](resources/template.md) for structure.
**Step 6: Validate quality**
Self-assess using [resources/evaluators/rubric_design_of_experiments.json](resources/evaluators/rubric_design_of_experiments.json). Check: objective clarity, factor completeness, design appropriateness, randomization plan, measurement protocol, statistical power, analysis plan, and deliverable quality. **Minimum standard**: Average score ≥ 3.5 before delivering.
## Common Patterns
**Pattern 1: Screening (many factors → vital few)**
- **Context**: 10-30 candidate factors, limited budget, want to identify 3-5 critical factors
- **Approach**: Plackett-Burman or fractional factorial (Resolution III/IV)
- **Output**: Pareto chart of effect sizes, shortlist for follow-up optimization
- **Example**: Software performance tuning with 15 configuration parameters
**Pattern 2: Optimization (find best settings)**
- **Context**: 2-5 factors already identified as important, want to find optimal levels
- **Approach**: Full factorial (2^k) or fractional factorial + steepest ascent
- **Output**: Main effects plot, interaction plots, recommended settings
- **Example**: Manufacturing process with temperature, pressure, time factors
**Pattern 3: Response Surface (map the landscape)**
- **Context**: Need to understand curvature, find maximum/minimum, quantify tradeoffs
- **Approach**: Central Composite Design (CCD) or Box-Behnken
- **Output**: Response surface equation, contour plots, optimal region
- **Example**: Chemical formulation with ingredient ratios
**Pattern 4: Robust Design (work despite noise)**
- **Context**: Product/process must perform well despite uncontrollable variation
- **Approach**: Taguchi inner-outer array (control × noise factors)
- **Output**: Settings that minimize sensitivity to noise factors
- **Example**: Consumer product that must work across temperature/humidity ranges
**Pattern 5: Sequential Experimentation (learn then refine)**
- **Context**: High uncertainty, want to learn iteratively with minimal waste
- **Approach**: Screening → Steepest ascent → Response surface → Confirmation
- **Output**: Progressively refined understanding and settings
- **Example**: New product development with unknown factor relationships
## Guardrails
**Critical requirements:**
1. **Randomize run order**: Eliminates time-order bias and confounding with lurking variables. Use random number generator, not "convenient" sequences.
2. **Replicate center points**: For designs with continuous factors, replicate center point runs (3-5 times) to estimate pure error and detect curvature.
3. **Avoid confounding critical interactions**: In fractional factorials, don't confound important 2-way interactions with main effects. Choose Resolution ≥ IV if interactions matter.
4. **Check design balance**: Ensure orthogonality (factors are uncorrelated in design matrix). Correlation > 0.3 reduces precision and interpretability.
5. **Define response precisely**: Use objective, quantitative, repeatable measurements. Avoid subjective scoring unless calibrated with multiple raters.
6. **Justify sample size**: Run power analysis to ensure design can detect meaningful effect sizes with acceptable Type II error risk (β ≤ 0.20).
7. **Document assumptions**: State expected effect magnitudes, interaction assumptions, noise variance estimates. Design validity depends on these.
8. **Plan for analysis before running**: Specify statistical tests, significance level (α), effect size metrics before data collection. Prevents p-hacking.
**Common pitfalls:**
- ❌ **One-factor-at-a-time (OFAT)**: Misses interactions, requires more runs than factorial designs
- ❌ **Ignoring blocking**: If runs span days/batches/operators, block accordingly or confound results with time trends
- ❌ **Too many levels**: Use 2-3 levels initially. More levels increase runs exponentially.
- ❌ **Unmeasured factors**: If an important factor isn't controlled/measured, it becomes noise
- ❌ **Changing protocols mid-experiment**: Breaks design structure. If necessary, restart or analyze separately.
## Quick Reference
**Key resources:**
- **[resources/template.md](resources/template.md)**: Quick-start templates for common designs (factorial, screening, response surface)
- **[resources/methodology.md](resources/methodology.md)**: Advanced techniques (optimal designs, Taguchi, mixture experiments, sequential strategies)
- **[resources/evaluators/rubric_design_of_experiments.json](resources/evaluators/rubric_design_of_experiments.json)**: Quality criteria for experiment plans
**Typical workflow time:**
- Simple factorial (2-4 factors): 15-30 minutes
- Screening design (8+ factors): 30-45 minutes
- Response surface design: 45-60 minutes
- Robust design (Taguchi): 60-90 minutes
**When to escalate:**
- User needs mixture experiments (factors must sum to 100%)
- Split-plot designs required (hard-to-change factors)
- Optimal designs for irregular constraints
- Bayesian adaptive designs
→ Use [resources/methodology.md](resources/methodology.md) for these advanced cases
**Inputs required:**
- **Process/System**: What you're experimenting on
- **Factors**: List of controllable inputs with candidate levels
- **Responses**: Measurable outputs (KPIs, metrics)
- **Constraints**: Budget (max runs), time, resources
- **Objective**: Screening, optimization, response surface, or robust design
**Outputs produced:**
- `design-of-experiments.md`: Complete experiment plan with design matrix, randomization, protocols, analysis approach
This skill helps you design, plan, and analyze experiments that vary multiple factors efficiently to find the vital few, optimize settings, or map response surfaces. It focuses on minimizing runs while preserving the ability to detect main effects and interactions. Use it to turn constrained testing budgets into reliable, actionable conclusions.
The skill guides you through defining objectives and constraints, listing factors and responses, selecting an appropriate experimental design (screening, factorial, response surface, Taguchi, etc.), and producing a randomized design matrix and execution protocol. It embeds guardrails like randomization, replication, blocking, and power justification so you can run experiments that support unbiased statistical analysis and robust recommendations.
Can I use DOE with very few runs?
Yes — screening designs like Plackett-Burman or fractional factorials are built for limited runs, but expect reduced ability to estimate interactions; follow up with focused experiments on the shortlisted factors.
How do I handle hard-to-change factors?
Use split-plot or nested designs to accommodate hard-to-change factors and adjust the analysis for different error structures; escalate to a specialized design if many hard-to-change factors exist.