I think that's exactly why theory people need to have real experimental expertise, so that they know how to reach out effectively to those folks, and how to frame their models (e.g. minimize equations in pubs) so that the exp. folk want to read them.
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I've work with data gathered by others. I've gathered data myself but only once and for an online thing (http://rescience.science ), so I don't really count it TBH. I have been involved in the design of experiments but never very very closely.
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Replying to @o_guest @bradpwyble and
But I have no idea if you consider me (I consider myself a modeller) a theory person and if you think I have "real experimental expertise"?
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Replying to @o_guest @bradpwyble and
Most people have experimental experience in the way I use these words due to the way cogsci and psych are taught. Modellers seem to emerge despite not because of most programmes — because the focus seems to be almost always empirical.
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Replying to @o_guest @bradpwyble and
So unless my experiences are somehow outlier ones (which they may be I realise I'm in a very small minority of people within cog/psych) I believe it's already the case that experimental experience is a baseline experience all PhDs have.
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Sometimes exp training can even hinder theoretical development. Not saying no empirical expertise is useful for theoretician, but also see how drilling of exp training can kill all conceptual creativity & make people conflate statistical hypothesis with substantive theory.
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If you're analyzing data, I think that's essentially empirical experience. I think the important aspect is just to know that data can be messy so that you don't get too attached to a particular interpretation of it.
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This is a good point, e.g. was at meeting with logicians & a cognitive scientist showed a plot of human data, with 3 noisy lines. I and other empirically trained ppl understood the general pattern. A logician asked "why do the lines zig-zag & how does your theory explain that?"
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Replying to @IrisVanRooij @bradpwyble and
Just saying: that some experience with the variability in data / phenomena of interest can give one a better sense of what pattern in the observations is in need of substantive theoretical explanation.
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Yeah, I've been where that logician was.
The only stats-related thing I knew before I moved to Psychology were Bayesian graphical models. I can't believe after more than a decade the module still exists and is taught by the same person! https://www.york.ac.uk/students/studying/manage/programmes/module-catalogue/module/COM00032H/2018-19 …2 replies 0 retweets 2 likes
To be clear, it's not 100% unchanged. It didn't used to have the phrase machine learning in it, for example. A clear demonstration of the effect of the current hype. 
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