experimental-design

1# Experimental Design
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3## Overview
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5The 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.
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7The three ideas behind almost every good design (Fisher's principles):
8- **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.
9- **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.
10- **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.
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12This 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.
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