The Importance and Neglect of Variation - part 1

Nov 25 2014 Published by under data analysis, statistics, Uncategorized

Within science, my old thesis advisor used to say, there are A-Sciences and B-Sciences. The names came from the Uni's classification of undergraduate requirements. Other than having the label "B", as opposed to "the more important science that gets neglected", he felt this was A Good Thing. In short, A-sciences depended on rules, laws, and invariants. Chemistry. If you take hydrogen and oxygen, and know the pressure and temperature, you will know the phase-state of the result. The stuff of the 17th century enlightenment.

On the other hand, evolution, ecology, astronomy, he said, were statistical sciences. It wasn't a matter of a single law or paradigm, it was a matter of which ones occurred most often. For the evolutionary biologists out there, there is old hat. Darwin depended on the existence of variation to drive evolution. Survival of the fittest implies a more fit and a less fit organism.

Of course, B-science was neglected. That was part of its glory. A-science with its bald white men in lab coats (back then), as compared to bearded white men in plaid flannel shirts with mountain climbing boots in the middle of the city (in case a mountain did appear in the middle of the city), was the dominant paradigm. We were the subversives. Current funding trends support that perspective, fueled by a justification for improving health.

So it is no big shock that an NPR story titled You might be surprised when you take your temperature maintains that the magic number of 98.6F is not so magic and that  "Baseline normal temperatures differ from person to person and from day to day". One of the two points in the article is a listing of temperatures that might mean something clinically (above 101.5 is a serious fever and multiple organ failures happen at 107F). The other of course is that normal is not a set number. Variation gets its due in a backhanded way.

I find the lack of attention to variation to range between irritating and bad science. I remember during my postdoc, a neuroscientist saying that something along the lines of "I measure single neurons" and that "variation is irrelevant as each neuron has an outcome". In my ill-equipped intellectual armamentarium at the time, I tried to point out there were many single neurons and how did he know that the one he measured was representative of all the neurons? And what about differences among neurons? I could not articulate the  value of concepts of variation vs. central location to all science, but that's where I was heading, even then. Sometimes what is important is not whether two things differ in central value (the mean, the median), but in how they vary around that value. Next post will look at that in some detail.

Meantime, an interesting book (technical) book on variation is: Variation: A Central Concept in Biology  2005 by  Benedikt Hallgrímsson (Editor), Brian K. Hall (Editor) ISBN-10: 0120887770 ISBN-13: 978-0120887774. Online ($$$) version here. Review here.

 

 

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Summary:Darwins theory of evolution by natural selection was based on the observation that there is variation between individuals within the same species. This fundamental observation is a central concept in evolutionary biology. However, variation is only rarely treated directly. It has remained peripheral to the study of mechanisms of evolutionary change. The explosion of knowledge in genetics, developmental biology, and the ongoing synthesis of evolutionary and developmental biology has made it possible for us to study the factors that limit, enhance, or structure variation at the level of an animals physical appearance and behavior. Knowledge of the significance of variability is crucial to this emerging synthesis. This volume situates the role of variability within this broad framework, bringing variation back to the center of the evolutionary stage.      

2 responses so far

  • Miss MSE says:

    Over here in engineering-land, we're starting to come to grips with the importance of variation in design and modeling. For years, the strategy has been to use the single-value "strength" and a factor of safety, but as parts are being more carefully optimized, variation is going to have to be incorporated. The amusing part to me is that failure analysis has always been a statistical endeavor, but somehow, that's been neglected by the other end of the process.

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