I think it's helpful to put on our statistics hats when looking at data like this... We have some observed values and a number of available covariates, which, perhaps, help explain the observed variability. Some legitimate sources of variation (eg, proximity to cooling in the NFS box, whether the hard drive was dropped as a child, stray cosmic rays) will remain obscured to us - we cannot fully explain all the variation. But when we average over more instances, those unexplainable sources of variation are captured as a residual to the explanations we can make, given the avialable covariates. The averaging acts a kind of low-pass filter over the data, which helps reveal meaningful trends.
Meanwhile, if we slice the data up three ways to hell and back, /all/ we see is unexplainable variation - every point is unique.
This is where PCA is helpful - given our set of covariates, what combination of variables best explain the variation, and how much of the residual remains? If there's a lot of residual, we should look for other covariates. If it's a tiny residual, we don't care, and can work on optimizing the known major axes.