Crack A/B Testing Problems for Data Science Interviews | Product Sense Interviews

Emma welcomes viewers to her channel, highlighting the popularity of her video on cracking product sense problems. She introduces the topic of A/B testing, which is often combined with metric questions in data science interviews. Emma plans to cover six key topics on A/B testing, provide common questions and answers, and share resources for further learning. She outlines the video's content, encouraging viewers to skip sections they are already familiar with. The first topic is a basic explanation of A/B testing, its importance in the industry, and its frequent appearance in data science interviews.

The second topic delves into the specifics of designing an A/B test, focusing on determining the test's duration. To decide this, one must calculate the sample size, which requires understanding type 2 error (or power), significance level, and the minimum detectable effect. Emma explains the 'rule of sum' formula for sample size and discusses how each parameter affects it. She also touches on the challenges of estimating the difference between treatment and control groups before running the experiment and the importance of the minimum detectable effect in practical scenarios.

Emma addresses the multiple testing problem that arises when conducting tests with multiple variants, explaining the increased risk of false discoveries and how it can be mitigated using methods like the Bonferroni correction or controlling the false discovery rate (FDR). She also discusses the primary and novelty effects that can influence user behavior in response to new features, and how these effects can cause initial test results to be misleading. Strategies to analyze and deal with these effects, such as focusing on first-time users or comparing different user groups, are suggested.

The fourth topic tackles the issue of interference between control and treatment groups, which can lead to unreliable test results. Emma provides examples from social networks and two-sided markets to illustrate how network effects and shared resources can influence user behavior and distort the treatment effect. She then presents various strategies to prevent such spillover, including geo-based randomization, time-based randomization, network clustering, and ego network randomization, each with its own applications and limitations.

In this section, Emma outlines a testing strategy for a new feature aimed at increasing the number of rides by offering coupons, focusing on preventing spillover effects between control and treatment groups. She suggests methods like geo-based randomization for two-sided markets and network clustering or ego network randomization for social networks. The goal is to isolate the effects of the new feature and accurately measure its impact, considering the unique challenges posed by each type of platform.

Concluding the video, Emma recommends two resources for further learning on A/B testing: a free online course from Udacity covering the fundamentals and a book titled 'Trustworthy Online Control Experiments' that provides in-depth knowledge on running effective A/B tests, potential pitfalls, and solutions. She also mentions her plan to summarize the book's content in a future video and invites viewers to share any questions they may have.