---
title: "Designing a Biological Experiment: Units, Controls and Randomisation"
description: "Before any measurement is taken, the design fixes what can be concluded: what the experimental unit is, what varies with it, and what is held fixed or randomised. This stop writes the design down whil"
canonical: https://lightmysky.com/learn/science/designing-a-biological-experiment-units-controls-and-randomisation-mt_Hc6BAdlkLf
source: https://lightmysky.com/learn/science/designing-a-biological-experiment-units-controls-and-randomisation-mt_Hc6BAdlkLf.md
retrieved: 2026-09-12
---

> **Agent view.** This is the Markdown twin of the page, for tools and assistants.
> When to use this site, and the call that answers each job: https://lightmysky.com/agent-instructions.md
> API description (OpenAPI 3.1): https://lightmysky.com/openapi.json · Authentication: https://lightmysky.com/auth.md
> Pricing: https://lightmysky.com/pricing.md · Catalog: https://lightmysky.com/llms.txt · Full catalog: https://lightmysky.com/llms-full.txt
> Every machine-readable file on this domain: https://lightmysky.com/.well-known/ai-catalog.json
> Ask for Markdown with `Accept: text/markdown`, a `.md` address, or `?mode=agent`.

# Designing a Biological Experiment: Units, Controls and Randomisation

Before any measurement is taken, the design fixes what can be concluded: what the experimental unit is, what varies with it, and what is held fixed or randomised. This stop writes the design down while the data do not yet exist.

Subject: Science · Area: Scientific Inquiry · Ages 22 to 23
Page: https://lightmysky.com/learn/science/designing-a-biological-experiment-units-controls-and-randomisation-mt_Hc6BAdlkLf

## Ready when they can

- Identify the experimental unit in a stated protocol and count it correctly
- Choose blocking or randomisation for a stated nuisance factor and justify the choice
- Name the control that rules out the alternative explanation a reviewer will raise first

## Lesson: Designing an experiment that can convince

You write the design before any measurement exists. You name the treatments, the units that receive them, and the response to measure. The experimental unit is the smallest thing independently assigned, and miscounting it is the classic error.

You defend the causal claim with two tools. Controls receive everything except the active factor, so any gap traces back to the treatment. Random assignment spreads unknown differences evenly, and replication across many units shrinks the role of luck.

**Example.** You test a feed additive on trays of twenty plants with one tray per dose. You refuse to call it n equals twenty per group, because the tray was assigned, not the plant. You average each tray to one value, then compare trays.

**Tip.** You match the layout to the nuisance you fear most. You block a known nuisance like clinic or batch by grouping similar units first and randomizing inside blocks. And you never confuse assignment with sampling: assignment defends the causal claim inside, sampling defends generalization outside.

**Recap.** You name the unit, you control and randomize, you block known nuisances, and you count only independent units.

## Practice

8 questions on this page, each with its working shown.

## Needs first

- [Errors, Power and the Design of a Test](https://lightmysky.com/learn/mathematics/errors-power-and-the-design-of-a-test-mt_D7ZXbvSD0l)
- [Fractionation, Blotting and Pulling Down a Binding Partner](https://lightmysky.com/learn/science/fractionation-blotting-and-pulling-down-a-binding-partner-mt_MRfFQlBIPy)

## Opens up

- [Pseudoreplication: When n Is Smaller Than It Looks](https://lightmysky.com/learn/science/pseudoreplication-when-n-is-smaller-than-it-looks-mt_2gRWB6RTaI)