Should the tag ab-test test be removed from the list or made into a synonym? It is jargon for the BI community and we should encourage use of proper technical terms to preserve the consistency of the field of statistics. Should we redirect to "hypothesis-testing" or some other existing tag? Is there a term in the community of statisticians for a multi-grouped randomized experiment where the hypothesis is that one is the best in a well defined metric?
Organizing tags is a delicate balancing act between being too comprehensive and too specific. It is no surprise, then, that discussions about tags occur frequently. I am grateful that our community is constantly thinking about them and proposing improvements.
Tags are primarily intended to help people find information. Secondarily, they organize threads into related groups that are used to recognize people who are active in various specialties.
The proposal to synonymize hypothesis-testing with ab-test runs counter to both these objectives. It loses the semantic differentiation implied by "A/B testing." It would seem prudent not to make one a synonym of the other.
Supporting this conclusion are 49 threads currently endowed with the ab-test tag. This number, which has developed organically by original posters and community members applying the tag as they see fit, is evidence that ab-test is perceived to be useful.
I don't really agree here. A/B testing is a specific kind of experimental design that is important and common in a specific setting (most typically website development, I gather). It could be a duplicate for experimental-design, but I don't think it's a duplicate of
[hypothesis-testing]. Moreover, I think it is specific enough, and used often enough, to merit its own tag; we do have tags for other specific designs (e.g., split-plot).
People working in particular areas tend to embrace slightly different terminology from people working in other areas, and both may use the terminology differently to mainstream statisticians.
So for example you might see 'force of decrement' used by an actuary (moreso in the past than now) where a statistician might say 'hazard', for example, and such differences persist in every corner of statistics.
These differences come in from psychology, biology, physics, astronomy, econometrics, marketing, and so on (and on).
I don't think it's really up to us to dictate what's 'proper' terminology -- if enough people are using it to talk to each other, even if it's in some small niche application area, it should as far as possible be embraced.
The main caveat would be in the case where there's a very strong chance that a terminology will be very directly misleading, in which case at the very least we need to be very careful to clarify what's going on and in some cases (as few as possible) perhaps call for going with the more conventional terminology.