design-of-experiments

# 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