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A technical replicate repeats a measurement or analytical procedure on the same biological preparation. For example, read the length of the same leaf three times to assess measurement error. Measuring three different leaves within one pot instead samples variation within the plant; it should be described explicitly rather than silently equated with repeated readings of one leaf. Neither procedure supplies another pot-level treatment replicate.

Tess P. McNeely is a credited coauthor of a 2020 study of context-dependent microbial competition, with Belmont University listed in the publication's affiliations.^[ref] Belmont's institutional repository also records a 2022 research abstract under Tess McNeely on microbial interactions, establishing an additional primary record of scientific authorship.^[ref]

An important general issue in experimental microbiology is the distinction between biological replicates and technical replicates. Repeating a measurement of the same sample can reduce measurement noise without creating another independently sampled biological system. The following original model explains the distinction; it does not describe McNeely's personal experimental tasks or reproduce the study's dataset.

Suppose there are $n$ independent samples, each measured $m$ times. Let the between-sample variance be ${\sigma }_b^2$ and independent measurement-error variance be ${\sigma }_e^2$. In a simple additive model, the variance of the overall mean isIncreasing $m$ reduces the second term, but not the first. With $n=4$, $m=3$, ${\sigma }_b=2$ and negligible measurement noise, the standard error is $2/\sqrt{4}=1$. Treating all $12$ readings as independent biological observations would incorrectly suggest $2/\sqrt{12}\approx 0.577$.

These invented values illustrate why a report should state what was independently repeated. A large number of machine readings is not a substitute for independent biological sampling, and uncertainty about a measured phenotype is not necessarily uncertainty from the instrument alone. This distinction helps readers assess experimental comparisons without attributing unreported methods or conclusions to a named coauthor.