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

AbstractIn 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. Physicians face a similar dilemma when comparing two results from the same patient. To determine whether the change between two values reflects disease progression or known analytical and physiological variation, the magnitude of the difference between two results is compared to the reference change value. These values are generated by quantifying analytical and within‐individual variation, and differences between two results from the same patient are considered clinically meaningful only if they exceed the combined effect of these two sources of ‘noise’. In this article, we describ