 Tess P. McNeely

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.