Definitive guide for choosing the right SE site to ask a question on?

Question

When you have a Data Science-related question, there are at least 7 relevant StackExchange sites to choose from. So how do you pick the right one?

Yes, for each question you can first ask on Meta (example: What it is the most appropriate Stack Exchange site to ask about ontologies and knowledge bases?), but that's not the best solution.

Can we create a definitive guide (think of a flowchart) that helps to choose the right SE site?

The list of relevant websites:

1. StackOverflow (most popular)
2. Data Science (current)
3. Cross Validated
4. Artificial Intelligence
5. Computer Science
6. Theoretical Computer Science
7. Computational Science

I assume that ideally we want to have only 1 correct website per question. All the questions asked on the incorrect websites should be closed/migrated to the correct ones. Otherwise we'll have confusion, duplicate questions and scattered user base. I think that a definitive guide would help both for asking new questions and for migrating the old ones.

Answer 1

Thanks for this. I love Big and Bold. But...

I assume that ideally we want to have only 1 correct website per question.

So concerning that, let's check the Definition of ideal:

n. A conception of something in its absolute perfection.

Perfection, by its nature, is rare... We can strive, but actually reaching that? Hmm.

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Moving on to the meat of your question:

Can we create a definitive guide (think of a flowchart) that helps to choose the right SE site?

I agree that a flow chart would be fabulous, so I want to beleive that the answer is Yes. But someone would need to generate it.

I doubt I am capable of it, would you like to give it a go?

Answer 2

You're right, these SEs overlap to a greater or lesser degree. I'd say use your judgment and post where it seems to fit best. Mods can migrate if they feel strongly about it.

I think the biggest ambiguities here are Cross Validated and Artificial Intelligence. Cross Validated tends to be more stats theory without, necessarily, reference to engineering of software that uses those ideas.

AI overlaps a lot and I personally disagree with it as a separate SE. Although you can define 'data science' and 'artificial intelligence' differently, in common usage, I find they're almost synonyms.