a/b testing with two different treatment groups I want to run an A/B test with two machine learning models M1 and M2 (called treatment groups T1 and T2), and I have a control group C without using machine learning model.

*

*Is there a specific name of these A/B test with two treatment groups and a control group?

*How do we show that M1 is really better than M2? E.g., using pairwise comparison using t-Test between T1, T2 and C or there are other methods?


A/B testing is a popular name for an experiment with two groups. If you have three groups, people sometimes refer to it as A/B/C testing, though the naming convention would get clumsy with more groups. The term is used in business, in statistics, this would be just called a randomized experiment.
If you have more than two groups, you need to use statistical tools for more than two groups like ANOVA rather than $t$-test. One of the reasons why A/B testing is more popular than using more groups is that planning and conducting an experiment is harder with more groups, it is also harder to interpret the results, and you need to use more sophisticated statistical tools. With more groups, you also need to consider the multiple-comparisons problem, etc. If you are not familiar with ANOVA, it would be covered in most of the applied statistics handbooks.
People use A/B testing because it is simpler to introduce and validate one improvement at a time.
