Experimental Design

---
name: experimental-design
description: Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
allowed-tools: Read Write Edit Bash
compatibility: Requires Python >=3.10. Scripts use numpy, pandas, and pyDOE3 (DOE matrices). Install with uv as shown below.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---

# Experimental Design

## Overview

The design of a study — how units are assigned to conditions, what is held constant, what is varied, and in what structure — determines what questions the data can answer. No analysis can rescue a confounded or pseudoreplicated design after the fact. This skill is about the decisions made *before* data collection: picking a design that isolates the effect of interest, randomizing to license causal claims, blocking to remove known nuisance variation, and structuring multi-factor experiments so effects are estimable rather than tangled together.

The three ideas behind almost every good design (Fisher's principles):
- **Randomization** — assign treatments at random so that confounders, known and unknown, are balanced in expectation. This is what turns a comparison into a causal claim.
- **Replication** — independent repetition at the right level, so you can estimate variability and your effects aren't artifacts of a single unit. The most common fatal error is **pseudoreplication**: counting repeated measurements on the same unit as independent replicates.
- **Blocking / local control** — group similar units (by batch, day, site, litter) and randomize within blocks, removing that nuisance variation from the error term instead of letting it inflate noise.

This skill helps you choose among design types, generate the actual randomization or DOE layout (with reproducible scripts), and avoid the structural mistakes that make data uninterpretable.

## When to Use This Skill

- Planning any comparative experiment or trial and deciding how to assign units… load the full skill through Skill Me