A/B Tests for Lyft Hardware

摘要

A/B tests, controlled experiments to measure performance differences between groups, are ubiquitous tools in data science and represent the gold standard in business measurement. At Lyft, A/B tests are found in nearly every product, from measuring the performance of marketplace algorithms and app designs, to engineering infrastructure changes. The units of analysis for most A/B tests are users, and sometimes markets or times of day. In this post, we are going to describe a new type of A/B test we are performing on our hardware team at Lyft. We will also illustrate a couple of A/B tests which we have done to safely and confidently improve the user experience, lower costs, and provide the best possible service for the cities in which we operate.

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