A/B Testing in Data Science Interviews by a Google Data Scientist | DataInterview

Summary

This video script offers a comprehensive guide to AB testing, a critical concept for data science interviews. It outlines the seven essential steps for conducting an AB test, using a real-life example of an online clothing store's new ranking algorithm. The guide covers understanding the problem, hypothesis testing, experiment design, data collection, validity checks, result interpretation, and decision-making. It emphasizes the importance of defining success metrics, avoiding biases, and considering business context alongside statistical significance for effective product improvement.

Takeaways

• 📘 AB testing is crucial for data science interviews, especially for companies like Google, Meta, and Uber, as it helps determine if changes are due to random chance or actual implemented changes.
• 🔍 Understanding the problem statement is the first step in AB testing, which involves clarifying questions and identifying success metrics and user journeys.
• ⚠️ Defining hypothesis testing is essential, setting up null and alternative hypotheses, and determining parameter values like significance level and statistical power.
• 🎯 Designing the experiment involves deciding the randomization unit, target user type, and other considerations to ensure a fair and effective test.
• 🔧 Running the experiment requires proper instrumentation for data collection and analysis without peeking at P-values to avoid biased decisions.
• 🤔 Conducting sanity checks or validity checks is vital to ensure the experiment's integrity and to avoid flawed results due to design flaws or biases.
• 📊 Interpreting results involves analyzing the direction of the success metric, considering the P-value for statistical significance, and assessing the confidence interval.
• 🚀 Making a launch decision requires considering the success metric, cost of launching, and the risk of committing a false positive, alongside the statistical results.
• 🛍️ A real-life example of AB testing is provided by an online clothing store looking to test a new ranking algorithm to improve product relevance and boost revenue.
• 📈 The success metric chosen for the example is revenue per day per user, which should be measurable, attributable, sensitive, and timely.
• 🔄 The iterative nature of AB testing allows for quick product improvements, with experiments typically lasting 1-2 weeks to account for various factors like day of the week effects.

Q & A

Why is AB testing considered essential in data science interviews?

AB testing is a popular topic in data science interviews, especially for companies like Google, Meta, and Uber. It is used to determine whether a change on a platform is due to random chance or the actual implementation of a new feature. Data scientists frequently use AB tests to make data-driven decisions, making it a crucial concept to understand.

What is the first step in setting up an AB test?

The first step in setting up an AB test is to understand the problem statement. This involves making sense of the case problem, asking clarifying questions, and identifying the success metric and user journey.

Why is it important to start with the business context in an AB testing interview question?

Starting with the business context helps ensure that you address the problem comprehensively. It sets the stage for the experimental design by understanding the product, the user journey, and the success metrics, which are crucial for accurate interpretation of the test results.

What is a null hypothesis in the context of AB testing?

In the context of AB testing, the null hypothesis states that there is no difference between the control and treatment groups—in this case, the average revenue per day per user between the Baseline and the variant ranking algorithms are the same.

How do you determine the sample size for an AB test?

The sample size for an AB test can be determined using the formula: n ≈ 16 * (variance / Delta²), where Delta represents the difference of the key metric between the treatment and control groups. This formula assumes a significance level of 0.05 and a statistical power of 80%.

Why should you avoid checking the P value before the experiment is completed?

Peeking at the P value before the experiment is completed can lead to incorrect conclusions. When the sample size is small, there is high variability, and prematurely checking the P value increases the risk of falsely rejecting the null hypothesis.

What is the purpose of performing validity checks after running an AB test?

Validity checks are performed to ensure that the experiment results are reliable. This includes checking for instrumentation errors, external factors that might have influenced the results, selection bias, and ensuring that the sample ratio between control and treatment groups is balanced.

What is a novelty effect, and how can it be detected in AB testing?

The novelty effect occurs when users react positively simply because they are exposed to something new, rather than the actual effectiveness of the change. It can be detected by segmenting users into new visitors and recurring visitors and comparing their responses to the change.

When is it advisable to rerun an AB experiment?

It is advisable to rerun an AB experiment if the confidence interval is wide or if the lower bound of the interval is not practically significant, but the upper bound is. Increasing the statistical power in a rerun can improve the precision of the results.

What are some business factors to consider before deciding to launch a change based on AB testing results?

Before launching a change, consider the trade-off between the success metric and secondary metrics, the cost of launching and maintaining the change, and the risk of committing a Type I error, which could lead to a negative impact on user experience.