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In general: When it is okay to cross post from Cross Validated or Stack Overflow to Data Science?

Specific Situation: I have a machine learning question on Cross Validated that does not seem to be getting any traction. When it is okay to cross post it to Data Science?

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Cross posting is a bad idea. It's rare that exactly the same question is on topic on both sites. Having the same question (and potentially the same answers) on two separate sites is splitting the knowledge. Stack Exchange is all about having one place where people can find the answer to their question.

At the very least you should be tailoring your question to each site, emphasising the parts that are particularly relevant for that site over the rest of the question. What would be ideal is that you wait for a while and then ask a new question using the information you've gained from the answers to the first on the most appropriate site.

If you really think that your question would do better on the other site then flag it for moderator attention. While migration to a(nother) beta site is discouraged - there's no migration path - it does happen.

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If its not getting flagged off-topic on CV then there's another reason its not "getting any traction". Maybe you have to accept its a poor question nobody wants to answer or offer comments for improvement?

I don't really see any reason to cross post (ie to both). Statistics is statistics, data science isn't. Statistics of big data? Well, the theory is still statistics, the practice is data science. The definition of the average number of words is statistics, counting them on Hadoop is data science.

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  • $\begingroup$ I don't think that the question is poor. It even has an upvote. I just think that there are few experts in the field. Here is the link: stats.stackexchange.com/questions/101237/… $\endgroup$ – power Jun 12 '14 at 3:34
  • $\begingroup$ I think with nearly 500 questions tagged "neural-networks" on CV you've found the right spot there. $\endgroup$ – Spacedman Jun 12 '14 at 8:00
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    $\begingroup$ Data science heavily relies on statistics. $\endgroup$ – Franck Dernoncourt Mar 7 '17 at 21:46

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