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Replication in experimental design Created 2026-10-07 Updated 2026-10-18
Replication in experimental design repeats a treatment on multiple independent experimental units, so that variation between those units can inform a treatment contrast and its standard error. Repeated measurements on one experimental unit can improve measurement precision without providing additional independent treatment replication. The experimental unit is identified by where the treatment is applied, rather than by how many responses are recorded.[ref]
Experimental unit (NC3Rs Experimental Design Assistant). Experimental unit, “Why is the experimental unit important?” and “Know your experimental unit”, revised 31 October 2023
For a constructed example, grow one plant in each of eight separate pots. Apply treatment A to four pots and treatment B to four pots, without sharing drainage water between pots. Measure the length of three leaves per pot. The pot is the experimental unit; the leaf is the observational unit. There are four treatment replicates per group, eight experimental units overall, and measurements. The three leaves in one pot are nested measurements, because they share the same plant and treatment application. The setup assumes that pots do not affect one another; simply putting them in separate containers does not establish statistical independence.
Constructed two-treatment design: eight pot experimental units contain twenty-four leaf observational units
. Each outlined pot receives one treatment application and supplies three nested measurements. A pot label marks an experimental unit, not a measured response value. Details.
Randomization chooses which experimental units receive which treatments; replication supplies multiple experimental units receiving the same treatment. These solve different problems. In the balanced allocation above, choose the four A pots uniformly among the possible subsets. A particular pot has probability of receiving A. Given that one pot receives A, a second receives A with conditional probability , rather than . Thus the assignments are not independent random variables, even though the experimental units can supply independent responses conditional on those assignments in a suitable statistical model.
An actual ecology experiment illustrates why the distinction matters. In Stuart H. Hurlbert's account, twelve experimental ponds initially received zero, fifty or 450 fish, with five, four and three ponds respectively. Preparation included assessing variation between ponds before introducing fish; allocation considered both location and existing differences between ponds. The zero-fish ponds supplied the scientific control. These were repeated treatment applications to ponds, not twelve samples from one pond.[ref]
Pseudoreplication and the Design of Ecological Field Experiments by Stuart H. Hurlbert. Pseudoreplication and the Design of Ecological Field Experiments, pp. 194–195, combined or hybrid allocation approach and the Hurlbert–Mulla experiment
The original article followed the southern California ponds for ten months. Its readouts included zooplankton populations and total phytoplankton; it reported decreases in some zooplankton populations and increased total phytoplankton.[ref]
Impacts of mosquitofish (Gambusia affinis) predation on plankton communities by Stuart H. Hurlbert and Mir Subhan Mulla. Impacts of mosquitofish (Gambusia affinis) predation on plankton communities, abstract, Hydrobiologia 83, pp. 125–151, August 1981
Repeated sampling within a pond helps describe its response through time, while ponds supply treatment replication. Unequal group sizes and initial pond variation matter for the statistical model; the treatment counts alone do not establish equal variances or independent responses.