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We have seen a massive growth of online experiments at LinkedIn, and inindustry at large. It is now more important than ever to create an intelligentA/B platform that can truly democratize A/B testing by allowing everyone tomake quality decisions, regardless of their skillset. With the tremendousknowledge base created around experimentation, we are able to mine throughhistorical data, and discover the most common causes for biased experiments. Inthis paper, we share four of such common causes, and how we build into our A/Btesting platform the automatic detection and diagnosis of such root causes.These root causes range from design-imposed bias, self-selection bias, noveltyeffect and trigger-day effect. We will discuss in detail what each bias is andthe scalable algorithm we developed to detect the bias. Surfacing up theexistence and root cause of bias automatically for every experiment is animportant milestone towards intelligent A/B testing.

Automatic Detection and Diagnosis of Biased Online Experiments