When neuroscience met clinical pathology: partitioning experimental variation to aid data interpretation in neuroscience

Abstract

In animal experiments, neuroscientists typically assess the effectiveness of interventions by comparing the average response of groups of treated and untreated animals. While providing useful insights, focusing only on group effects risks overemphasis of small, statistically significant but physiologically unimportant differences. Such differences can be created by analytical variability or physiological within-individual variation, especially if the number of animals in each group is small enough that one or two outlier values can have considerable impact on the summary measures for the group.

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