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arXiv · 2312.14534

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

Zheng Cai·Bo Hu·Zhihua Zhu
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Abstract

Online experiments are widely used for improving online services. While doing online experiments, The student t-test is the most widely used hypothesis testing technique. In practice, however, the normality assumption on which the t-test depends on may fail, which resulting in untrustworthy results. In this paper, we first discuss the question of when the t-test fails, and thus introduce the rank-sum test. Next, in order to solve the difficulties while implementing rank-sum test in large online experiment platforms, we proposed a global-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 has higher statistical power than the t-test, but also more time efficient than the traditional rank-sum test, which eventually makes it possible for large online experiment platforms to use.

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Zheng Cai, Bo Hu, Zhihua Zhu. 2023-12-22. Global Rank Sum Test: An Efficient Rank-Based Nonparametric Test for Large Scale Online Experiment. https://arxiv.org/abs/2312.14534

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