| Summary: | "Deciding whether or not to launch a new product or feature is a resource management bet for any Internet business. Conducting rigorous online A/B tests flattens the risk. Drawing on her experience at Airbnb, data scientist Lisa Qian offers a practical ten-step guide to designing and executing statistically sound A/B tests. Discover best practices for defining test goals and hypotheses; Learn to identify controls, treatments, key metrics, and data collection needs; Understand the role of appropriate logging in data collection; Determine how to frame your tests (size of difference detection, visitor sample size, etc.); Master the importance of testing for systematic biases; Run power tests to determine how much data to collect; Learn how experimenting on logged out users can introduce bias; Understand when cannibalization is an issue and how to deal with it; Review accepted A/B testing tools (Google Analytics, Vanity, Unbounce, among others)."--Resource description page
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