<p>Spoiler: a highly opinionated answer follows.</p>

<p>I am here with <a href="https://stats.meta.stackexchange.com/a/2027/5739">Peter Flom</a>. I think questions on how to handle stuff in statistical packages deserve a site that is separate from CV and separate from SO. I would be the first to admit that I am behind on any of the Data Science issues involving Hadoop or Mahout -- I don't get to see the limits of my existing tools in the work that I have, and however much I would love to learn Python and such, I don't have the need for the applications that I am an expert in, and thus have no pressure to learn them.</p>

<p>Here's how it settles in my head (which is the sample of size 1, and there are 4000 heads on CV meta, most of which are undoubtedly better than mine). To me, programming is figuring out how compilers work, and what they allow you to do, almost regardless of the input data (and a good program will protect itself from poor data, anyway). Most statistical packages (excluding probably R) lack the syntax features to express these levels of abstraction from the data. On the contrary, in data analysis tasks, we usually have to take data for granted, and dance around it, may be with some reshaping or contracting of the data or construction of some new variables to better explicate the existing data structures (and the vast majority of the real life data sets represent a single rectangle -- medical patients and their vitals, say; sometimes two or three rectangles -- patients plus hospitals plus insurance companies). (Going back to my ignorance comment in the first paragraph -- this is the kind of data that I deal with, not the jagged, multiple sites twitter threads that many people fancy to mine.) So R or SAS or Stata questions on SO have looked out of place to me all the way along.</p>

<p>There has been a very recent discussion about this on Statalist, the support forum for Stata software. Due to some planned hardware and personnel retirement, this whole list is being transplanted from <a href="http://www.stata.com/statalist/archive/" rel="nofollow noreferrer">a text-only email version</a> to a <a href="http://statalist.org/forums" rel="nofollow noreferrer">forum version</a>. <a href="http://www.stata.com/statalist/archive/2014-04/msg00112.html" rel="nofollow noreferrer">My wishful thinking</a> was, though, that the mass of Stata users (several hundred, I am guessing) could move to StackExchange, for the mutual benefit. The Statalist users will get a functional platform that works well in improving answers and collecting knowledge. CV will get a group of users with high technical expertise (although some of the most active ones are already here; you can easily identify them in the <a href="https://stats.stackexchange.com/tags/stata/topusers">appropriate tag</a> activity).</p>

<p>As far as duplication of the content goes, there has been discussion of how to split mathematical statistics questions <a href="https://stats.meta.stackexchange.com/q/1929/5739">between CV and math</a>, and <a href="https://stats.meta.stackexchange.com/q/2022/5739">what to do with education questions</a>. Duplication is probably unavoidable; however in case of the overlap with math, at least math people are aware that theoretical statistics is present on CV. Whether the data science people are aware that A/B tests <a href="http://en.wikipedia.org/wiki/Student%27s_t-distribution#History_and_etymology" rel="nofollow noreferrer">have been invented some hundred years ago</a> is an open question to me; some are, but many probably aren't. What to do with the data science movement is <a href="http://magazine.amstat.org/blog/2013/07/01/datascience/" rel="nofollow noreferrer">an open question for professional statistical societies</a>, but that's a wave that statisticians cannot steer even the slightest. Extrapolating this experience here, I would expect that the CV users won't be able to stop the Data Science Area 51 from whatever trajectory it is on (although comparisons with Big Data proposal that never took off the ground are very obvious), especially given a much greater mass of programmers-proper in the StackExchange system as a whole compared to the mass of statisticians-proper on CV, and a much lower reputation of whuber compared to Jon Skeet. If anybody will have the patience to sift through the Data Science questions and point to the appropriate duplicates on CV, I guess we should give them a platinum badge of a saint.</p>
