design-of-experiments

What this skill does

# Design of Experiments

## Table of Contents
- [Workflow](#workflow)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#quick-reference)

## 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

**Design 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. **Preserve critical interactions**: In fractional factorials, avoid confounding important 2-way interactions with main effects. Choose Resolution IV or higher 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 (beta at most 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 (alpha), effect size metrics before data collection to prevent 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, analys