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Abstract:
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Technology companies, large and small, innovate both their internal operations and consumer facing products using online, randomized controlled trials, also known as A/B tests. Scaling A/B testing and other experimentation paradigms to meet the requirements of modern technology companies comes with a wide variety of statistical, computational, educational, and cultural challenges.
This session will bring together well-known practitioners of high frequency and big-data A/B experiments from a number of major technology companies. The discussion will focus broadly on the topic of scale, including the statistical methodology and computational challenges posed by massive data sets and a high throughput of experiments; how to unlock data scientists via platform-level solutions; and associated cultural and educational challenges in an environment of automated, or automation-assisted, decision making.
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