--- title: "Pseudoreplication: When n Is Smaller Than It Looks" description: "Pseudoreplication treats dependent measurements as independent units. Distinguish technical repeats, biological samples and treatment assignments, then use an analysis that accounts for the grouping i" canonical: https://lightmysky.com/learn/science/pseudoreplication-when-n-is-smaller-than-it-looks-mt_2gRWB6RTaI source: https://lightmysky.com/learn/science/pseudoreplication-when-n-is-smaller-than-it-looks-mt_2gRWB6RTaI.md retrieved: 2026-11-12 --- > **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`. # Pseudoreplication: When n Is Smaller Than It Looks Pseudoreplication treats dependent measurements as independent units. Distinguish technical repeats, biological samples and treatment assignments, then use an analysis that accounts for the grouping in the design. Subject: Science · Area: Scientific Inquiry · Ages 22 to 23 Page: https://lightmysky.com/learn/science/pseudoreplication-when-n-is-smaller-than-it-looks-mt_2gRWB6RTaI ## Ready when they can - Given a protocol, identify independently assigned units and the levels of variation the analysis must account for - Explain when to average repeated measurements within a unit and when a hierarchical analysis can retain them - Identify nesting in a figure legend and check what the reported n and error bars describe ## Lesson: Counting only truly independent replicates Pseudoreplication treats dependent measurements as independent units in an analysis. Several leaves from a plant or cells from a well can share conditions. These measurements may add information about that unit, but they do not create new independent treatment assignments. Identify the assignment level and relevant sources of dependence before choosing the analysis. Technical repeats remeasure the same specimen or preparation; subsamples measure parts within it. Biological samples are distinct specimens or separately prepared cultures and can share a donor or batch. Separate treatment assignments do not remove shared-source variation. Average suitable repeats into a unit summary, or use a hierarchical model that retains measurements and accounts for grouping. Changing an analysis to account for dependence can change its uncertainty without changing the estimated treatment difference. A result may then cease to meet the planned significance threshold. That does not prove the treatment effect is zero. Report the estimate, uncertainty, assignment units and model rather than treating significance as a verdict on whether an effect exists. *(drawing: Students measured within an assigned class do not create more class-level treatment assignments.)* For independent observations, the standard error of a mean is estimated by s divided by the square root of n. Positive within-unit correlation can make a calculation using every reading as independent underestimate that error. Not all error bars show standard errors: they may show standard deviations, confidence intervals or another quantity. Check the definition before interpreting their width. Repeated readings within independently assigned patients form a hierarchy. An analysis can use patient summaries or model variation between patients and dependence within patients. The choice depends on the response and question. Retain unit identifiers. Pooling every reading as an independent patient ignores the design, while discarding whole groups is not a general solution. A figure legend should distinguish assigned units from measurements. Many points from a few animals show nesting, but do not by themselves prove an invalid analysis. Check what n counts, what each point represents and how error bars were calculated. A report may show all readings while correctly using animals as the independent treatment units. **Recap.** Count independent assignments separately from measurements. Average suitable repeats or model their grouping. Check what n, plotted points and error bars describe before interpreting a result. *Original lesson, questions and flashcards about pseudoreplication, authored for this learning dataset.* ## Practice 8 questions on this page, each with its working shown. ## Needs first - [Designing a Biological Experiment: Units, Controls and Randomisation](https://lightmysky.com/learn/science/designing-a-biological-experiment-units-controls-and-randomisation-mt_Hc6BAdlkLf) ## Opens up - [Power and Effect Size, Decided Before the Data](https://lightmysky.com/learn/science/power-and-effect-size-decided-before-the-data-mt_zYNmSWAJT8)