Video description
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.
Publisher resources
Table of contents
- Overview of the Course
- Why Should You Run A/B Tests?
- The 10 Steps and An Overview of Case Studies
- Case Study 1: Red vs. Green Button
- Case Study 2: Testing a New Landing Page
- Case Study 3: Price Recommendations on an Online Marketplace
- Summary: Setting Up an A/B Test
- Some of Your Options
- Scaling A/B Testing and Developing a Culture of Experimentation
Product information
- Title: A/B Testing, A Data Science Perspective
- Author(s):
- Release date: September 2015
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491934777
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