In response to falenas108's "Ask an X" thread. I have a PhD in experimental particle physics; I'm currently working as a postdoc at the University of Cincinnati. Ask me anything, as the saying goes.
This is an experiment. There's nothing I like better than talking about what I do; but I usually find that even quite well-informed people don't know enough to ask questions sufficiently specific that I can answer any better than the next guy. What goes through most people's heads when they hear "particle physics" is, judging by experience, string theory. Well, I dunno nuffin' about string theory - at least not any more than the average layman who has read Brian Greene's book. (Admittedly, neither do string theorists.) I'm equally ignorant about quantum gravity, dark energy, quantum computing, and the Higgs boson - in other words, the big theory stuff that shows up in popular-science articles. For that sort of thing you want a theorist, and not just any theorist at that, but one who works specifically on that problem. On the other hand I'm reasonably well informed about production, decay, and mixing of the charm quark and charmed mesons, but who has heard of that? (Well, now you have.) I know a little about CP violation, a bit about detectors, something about reconstructing and simulating events, a fair amount about how we extract signal from background, and quite a lot about fitting distributions in multiple dimensions.
Reflecting some more here (I hope this schizophrenic little monologue doesn't bother anyone), I notice that none of this would trouble a pure computer scientist / reductionist:
"Probability? Yeah, well, I've got pseudo-random number generators. Are they 'random'? No, of course not, there's a seed that maintains the state, they're just really hard to predict if you don't know the seed, but if there aren't too many bits in the seed, you can crack them. That's happened to casino slot machines before; now they have more bits."
"Philosophy of statistics? Well, I've got two software packages here: one of them fits a penalized regression and tunes the penalty parameter by cross validation. The other one runs an MCMC. They both give pretty similarly useful answers most of the time [on some particular problem]. You can't set the penalty on the first one to 0, though, unless n >> log(p), and I've got a pretty large number of parameters. The regression code is faster [on some problem], but the MCMC let's me answer more subtle questions about the posterior.
Have you seen the Church language or Infer.Net? They're pretty expressive, although the MCMC algorithms need some tuning."
Ah, but what does it mean when you run those algorithms?
"Mean? Eh? They just work. There's some probability bounds in the machine learning community, but usually they're not tight enough to use."
[He had me until that last bit, but I can't fault his reasoning. Probably Savage or de Finnetti could make him squirm, but who needs philosophy when you're getting things done.]
Well, among others, someone who wonders whether the things I'm doing are the right things to do.