Why don't experimental physics groups have statisticians in it? Perhaps someone can clear up a bit a cognitive dissonance I am experiencing.  Pollsters are under constant scrutiny of statisticians for even the most mundane of survey topics.  With so much riding on the results of fundamental physics experiments, why don't we need statisticians to do the data analysis for us (or at least be looking over our shoulders)?
 A: Maybe for the same reason that experimental physics groups do not have a theoretician as a group member. 
One could think of experimental groups as ruled by  "control freaks", they need as members experimetnalists who have mastered enough theory to set up the experiments and enough theory to interpret the results. Within this "theory" one could count statistics as the simpler one to master.
A: In a number of areas in experimental physics you simply do not need a detailed statistical view on your data. 
At the research institutes I have visited there were a great number of experimental and theoretical physicists but basically nobody that does anything that would need a deep understanding of statistical methods. While in some disciplines like particle physics the experiments and data often consists of counting events or occurrences this is not true for a lot of other disciplines. As an example we are measuring resistance of metallic samples at low temperatures. We could measure the resistance 50 times per second, build a statistical model and so on, but we do not bother and it would be foolish. If the noise does not follow a gaussian distribution something is just wrong with our setup but not with the properties of our samples. 
In many cases the possible systematic errors are much higher in magnitude and severity that it is a lot more efficient to minimize those than to reduce the statistical error by advanced mathematical methods.
A: One of the characteristics of physics research is the regular use of advanced methods and techniques from other fields, such as mathematics, computer science, probability theory and even biology. Physicists therefore often need to dive deep into complex topics in other fields.
None of these topics is trivial and the level of understanding of physicists in these topics is as widely distributed as it is with complex 'hard core' physics topics.
A: Because physicists does not need most of statistics -- they only do hypothesis testing, with systems following simple laws and with directly measured, uncluttered data.
A: If your experimental result is shown to have a statistical significance of 3 sigmas only by employing the brightest statisticians using the most cutting-edge modern techniques, while the more pedestrian statistical techniques that a typical physicist knows only produce 2 sigmas, then you have trouble convincing the wider community of the real importance of your experiment.
While arguing over the detailed statistic techniques can affect the statistical significance level by some  amount, improving your experimental methods potentially will boost your result much more dramatically. It's good for physicists to focus on the latter rather than the former.
P.S. The most convincing experiments are those that don't require statistics at all. When an astronomer claims he has discovered a star, he typically doesn't quote any statistical significance level. When the significance is above 20, or even 100 sigmas, the word "statistics" simply disappears for good.
A: Because physicists learn the math and do it themselves. Why do you need a special expert class of people nowadays?
EDIT: Deconstructing statistics
In response to comments that "statisiticians go through years of study", I would like to say why I think all this studying is counterproductive. The theory of statistics (when it isn't about statistical mechanics or pure mathematical measure theoretic things) is usually concerned with the inference problem--- what is the likelyhood of a parameter to be x when a measured quantity correlated with the parameter is measured to be $y_1,y_2,...,y_n$ in a sequence of trials.
The complete solution to this problem is given by Bayes's theorem: the probability that the underlying parameter has value x is given by the probability that this value x will produce the experimental results $y_1,y_2,..,y_n$ conditioned by the prior knowledge which gives you some distribution on x to begin with.
Because Bayes's theorem solves the problem of inference so simply and naturally, the field of statistics is almost entirely built on rejecting it. Most of the field is based on the idea that one should not do Bayesian inference for one cockamamie reason or another, usually based on some silly philosphy which rejects priors or rejects the notion of a fundamental a-priori notion of probability. Because of this, physicists never learn Baysian inference from a class, they have to rediscover it for themselves (I certainly did, and most other people who do inference do too).
This means that if you hire a statistician, they will most often find lousy workarounds for Baysian methods, which will be useless to the experimental physicist. The issue is deeply ingrained--- many famous topics in statistics, like the sufficient statistics or the t-test, are born of the quest for a non-Baysian inference This quest is misguided, and will waste the experimental physicist's time. Within statistics, however, anti-Baysianism is a useful motivation for new results, so the field is dominated by anti-Bayesians.
It is also true in Biology. There, the Baysian method is (with difficulty) replacing statistician's pet inference methodologies. This diatribe is based on experience from about a decade ago, and might be out of date.
