Common Pitfalls of A/B Testing and How to Avoid Them
April 26, 2017 | 10am-11am PDT
A recording of this webinar will be made available within a week of the live session.
A/B testing is the process of using randomized controlled experiments on a web page or technology platform to determine which of two versions has a higher conversion rate. While this method of testing has become ubiquitous, there remain some common traps to which even the most sophisticated data scientists fall prey.
Join Ramesh Johari, Associate Professor, Stanford Department of Management Science & Engineering, as he discusses some common practices and pitfalls in A/B testing.
You will learn:
Join Ramesh Johari, Associate Professor, Stanford Department of Management Science & Engineering, as he discusses some common practices and pitfalls in A/B testing.
You will learn:
- To analyze the output of A/B tests
- To balance quick results with statistical significance during continuous monitoring
- To maintain data integrity when running simultaneous experiments
About the Speaker
Ramesh Johari is interested in the design and management of large-scale complex networks, such as the Internet. Using tools from operations research, engineering, and economics, he has developed models to analyze efficient market mechanisms for resource allocation in networks.Presented By
The Stanford Management Science & Engineering and the Decision Analysis graduate certificate programs.
Questions?
Please contact us at [email protected] or 650-204-3984.
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