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Online experiments are widely used for improving online services. While doingonline experiments, The student t-test is the most widely used hypothesistesting technique. In practice, however, the normality assumption on which thet-test depends on may fail, which resulting in untrustworthy results. In thispaper, we first discuss the question of when the t-test fails, and thusintroduce the rank-sum test. Next, in order to solve the difficulties whileimplementing rank-sum test in large online experiment platforms, we proposed aglobal-rank-sum test method as an improvement for the traditional one. Finally,we demonstrate that the global-rank-sum test is not only more accurate and hashigher statistical power than the t-test, but also more time efficient than thetraditional rank-sum test, which eventually makes it possible for large onlineexperiment platforms to use.

Global Rank Sum Test: An Efficient Rank-Based Nonparametric Test for Large Scale Online Experiment