The number of connections it accepted was not enough for the load, so it would queue the requests.
The table below provides additional test result details: The test above, which compares average posting rates between groups, uses a simple Student's t-test for determining statistical signficance.
These A/B tests help analysts and product managers better understand a feature's effect on user behavior and the overall user experience.
This case focuses on an improvement to Yammer's core “publisher”—the module at the top of a Yammer feed where users type their messages.
(Some argue that one-tailed tests are better, however.) You can read more about the differences between one- and two-tailed t-tests here.
Once you're comfortable with A/B testing, your job is to determine whether this feature is the real deal or too good to be true.