Is "Permutation Test" sufficient for a/b testing? I read the "Practical Statistics for Data Scientists" book which is written by Peter Bruce and Andrew Bruce. In this book, the hypothesis testing is only realized with permutation test. If I searched the web, there are formulas, also I did not see too many permutation test blog articles. I want to ask that the permutation test is enough to analyzing a/b experiment data, or do I need different thing ?
Sorry for my grammatical mistakes, my native language is not English.

It depends. That’s a very broad question.
Permutation testing is one way, of many, for hypothesis testing. In contrast to the more common tests based on sampling distribution, permutation tests tend to be more conservative to accept new evidence (lower power) and less sensitive to assumptions about the population. A downside of these tests is they’re very computationally intensive and impractical for medium or large datasets.
So permutation tests are appropriate for A/B testing if you’re willing to make these trade offs.
