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Introduction to Experimentation Questions

A/B tests are widely used to incrementally improve performance over time, particularly for digital marketing, product development, and website optimization.

A/B testing is a specific application of hypothesis testing commonly used by data scientists, especially for inference and product data science roles, to understand the performance of different variations of new features, or improvements to existing features.

A social media platform can launch an A/B test after changing the design of the "Like" button to assess how key user engagement metrics change. The company can use these results to decide whether to launch the new design.

What to expect

Questions related to this topic are usually asked in a separate experimentation round or in the form of applied case studies.

Example questions include:

  • How will you test if launching feature X is a good idea?
  • How will you determine the success of feature X?
  • Design an experiment to evaluate the effectiveness of X feature.
  • Given a scenario and XYZ data points, would you recommend launching this feature?

Since A/B testing questions are usually open-ended, candidates are expected to:

  • Convert an ambiguous business problem into concrete hypotheses with appropriate metrics
  • Understand the end-to-end process and practical considerations of setting up and analyzing an A/B test
  • Understand the underlying statistical concepts at each step of the test design

How to prepare

  • Review the underlying statistical concepts. Review the fundamental statistical concepts from previous lessons.
  • Set up and analyze A/B tests. Try running a real-world A/B test. Reading through the docs of an A/B testing platform is also a good way to familiarize yourself with the process. Utilize best practices and be aware of common pitfalls.
  • Review case studies and stay updated on industry trends. Study real-world examples and case studies of successful A/B tests in various industries. Follow industry blogs, forums, and publications to stay up-to-date with the latest developments in A/B testing and experimentation.
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