AB testing and hypothesis testing I have an entire market for which I plan to run two offers. 
I randomly split the market 50/50. All of Group A receives offer 1 and all of group B receives offer 2. 
I want to compare proportions of converters and revenue generated from the two groups
Are the groups A & B a "sample" or a "population" now?
When comparing proportion of converters or revenue from the two groups do I need hypothesis testing or is it enough to state the obvious eg Group A 30% converted vs Group B 20% converted and therefore offer 1 performed better than offer 2
 A: Unless we are dealing with a isolated set of individuals that is stable across time we are dealing with a sample. Here, as the problem statement particularly refers to "an entire market" and a market is not an inherently stable construct, it would be better to refer to sample or at least "accessible population". If anything this will give us flexibility if a new customer join the market or old customer depart and will also allows us to naturally refer to sampling error. This is important even in the case mentioned (Group $A$ has 30% conversion and group $B$ has 20% conversion) as the conversion estimates are still subjected to sampling variability.
CV.SE has a really good thread on the matter aptly title: "What is the difference between a population and a sample?". 
To summarise: unless we are in a highly idealised situation, where we expected little to no changes (either from external factors coming into play or just because of time evolution) I would refrain from using the term "population" without some further qualification like "target" or "accessible" population.
