InfiniteSkills - A/B Testing, A Data Science Perspective
English | .FLV | aac, 44100 Hz, stereo | h264, yuv420p, 1280x720, 15.00 fps(r) | 698MB
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)
Lisa Qian focuses on search and discovery at Airbnb. She has a PhD in Applied Physics from Stanford University.
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