>>>>> "Rolf" == Rolf Turner <r.tur...@auckland.ac.nz> writes:
> My impression --- and I could be wrong --- is that > physicists understanding of randomness is very narrow and > constrained. They tend to think along the lines of chaotic > dynamical systems (although perhaps not consciously; and > they may not explicitly express themselves in this way). > They also tend to think exclusively in terms of measurement > error as the source of variability. Which may be > appropriate in the applications with which they are > concerned, but is pretty limited. Also they're a rather > arrogant bunch. E.g. Rutherford (???): ``If I need > statistics to analyze my data I need more data.'' This is an interesting discussion all around, but as one of those physicists I feel a need to jump back in ;-) Just as in any multidisciplinary endeavor, much of the fun comes from bridging communication gaps that arise from our certainty that "everyone knows" what we mean when we say what we say. First, I counter with a quote from my list of interesting sayings :-) "We must be careful not to confuse data with the abstractions we use to analyze them." --- William James I went through an interesting transition when I moved from basic physics (medium energy nuclear/particle physics) to biomedical applications (cardiology and then imaging sciences/radiology). There is an important difference between physics-y statistical analysis and biomedical-y statistical analysis that I was not aware of before I crossed over to the biomedical side. That my biomedical and biostatisticians colleagues didn't have the same background didn't make their perspective invalid, just as my not having a background in biomedical statistics didn't make me arrogant. That we were unaware that we were sometimes speaking different languages made up of the same words lead to some adventures. I had to learn two things. One, that biomedical systems tend to have broad distributions while many physical systems have very narrow distributions. Two, that physics models are based on physics theories and that biomedical/biostats models are purely phenomonological and only model the data - they often do not have a basis in underlying physical theory. Simple, but not stressed in my statistical physics or biomedical statistics training. Perhaps the key example is statistical mechanics, both classical and quantum mechanical. A fundamental physics-y concept is that a single object has no statistical properties. "Statistical" is a word reserved for properties of ensembles. Statistical mechanics can only be applied to ensembles of objects where their joint behavior leads to (highly) predictable results. The density of states for any macroscopic ensemble of like objects is extremely sharply peaked, leading to wonderfully reliable theoretical predictions. Just the opposite of what we tend to see in biomedical systems. For those who are interested in a physics-y perspective, I'd suggest taking a crack at "Statistical Methods in Experimental Physics" (F. James) and some of the many statistical mechanics texts out there. My favorites are still F. Mandl's "Statistical Physics" and K. Huang's "Statistical Mechanics," but there are many, many more. Another nice little book is "Observational Foundations of Physics" by Cook. It addresses in part the question of why mathematics is so startlingly effective in physics. It is a result of the correspondence between physical processes in the natural world and mathematical groups. As far as I know, a similar correspondence does not exist in the biomedical realm, nor in many other domains. That lack of correspondence leads to purely phenomonological models that model the data but are not based on underlying physical theory - all that is left is statistical modeling. I suspect this is the source of the sort of statement you attributed to Rutherford. I hear him simply saying that we can do perfectly respectable statistical modeling without physics, but then it is not physics. And if our goal is to do physics, then we aught to get back to the lab and observe reality some more. Which is where the fun is for many of us scientists! Regards, Mike -- Michael A. Miller mmill...@iupui.edu Department of Radiology, Indiana University School of Medicine ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.