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

DataInterview

25 Feb 202220:13

EducationalLearning

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TLDRThis video script offers a comprehensive guide to AB testing, a crucial concept for data science interviews at companies like Google and Uber. Host Dan, a former Google and PayPal data scientist, walks viewers through a real-life AB test case, outlining the seven essential steps, from understanding the problem to making a launch decision. He emphasizes the importance of defining success metrics, conducting validity checks, and considering business context alongside statistical results to determine whether to implement changes.

Takeaways

📝 AB testing is crucial for data science interviews at companies like Google, Meta, and Uber as it helps determine the impact of changes on platforms.
🔍 The first step in AB testing is understanding the problem statement and clarifying the success metric and user journey.
⚠️ Defining hypotheses is essential, including setting up null and alternative hypotheses and determining parameter values like significance level and statistical power.
🎯 Experiment design involves deciding the randomization unit, target user type, and other considerations to ensure a fair test.
🔧 Running the experiment requires proper instrumentation for data collection and analysis without prematurely checking results.
🤔 Validity checks, such as sanity checks and bias assessments, are vital before interpreting results to ensure the experiment's integrity.
📊 Interpreting results involves analyzing the direction and significance of the success metric, considering P values and confidence intervals.
🚀 The decision to launch changes based on AB test results should consider metric trade-offs, costs, and the risk of committing a false positive.
📈 The success metric chosen for AB testing should be measurable, attributable, sensitive, timely, and have low variability.
🛠️ A/B testing is an iterative process aimed at quickly improving products, necessitating a balance between statistical significance and practical significance.
💡 Preparing for AB testing interviews involves understanding the core product features and user journey, which can be helpful in designing effective tests.

Q & A

What is the importance of AB testing in data science interviews?
-AB testing is a crucial concept in data science interviews because it is widely used by companies like Google, Meta, and Uber to determine if changes on their platforms are due to random chance or actual implemented changes.
Who is the presenter of the video and what are his credentials?
-The presenter is Dan, the founder of datainterview.com, a former data scientist at Google and PayPal, who provides insights on AB testing for data science interviews.
What are the seven steps involved in the AB testing procedure as outlined in the video?
-The seven steps are: 1) Understanding the problem statement, 2) Defining hypothesis testing, 3) Designing the experiment, 4) Running the experiment, 5) Conducting validity checks, 6) Interpreting the results, and 7) Making a launch decision.
Why is it important to understand the user journey when setting up an AB test?
-Understanding the user journey is important as it helps in defining the success metric, target user population, and determining at what stage the user should be considered a participant for the experiment.
What is a success metric in the context of AB testing?
-A success metric is a measurable attribute, such as revenue per day per user, that you aim to move positively to confirm that the applied change is beneficial for the platform.
What are the qualities to consider when defining a success metric for an AB test?
-The qualities to consider are measurability, attributability, sensitivity, and timeliness of the metric.
What is the significance of setting a significance level and statistical power in an AB test?
-The significance level (alpha) is the threshold for determining statistical significance, while statistical power (usually set at 80%) is the probability of detecting an effect if the alternative hypothesis is true.
Why is it recommended not to peak at the P-value during the experiment?
-Peaking at the P-value during the experiment can lead to premature conclusions and increase the chance of falsely rejecting the null hypothesis due to variability in the data.
What are some of the validity checks performed after running an AB test?
-Validity checks include ensuring instrumentation effects, external factors, selection bias, simple ratio mismatch, and novelty effect are accounted for to ensure the experiment's results are reliable.
What factors should be considered when making a decision to launch a change based on AB test results?
-Factors to consider include metric tradeoffs, the cost of launching, and the risk of committing a false positive (Type I error).
How does the video suggest dealing with different ranges of lift and confidence intervals in the context of making a launch decision?
-The video suggests considering the practical significance of the lift, the width of the confidence interval, and whether the bounds are within positive or negative territory to decide whether to launch, iterate, or scrap the idea.

Outlines

00:00

📊 AB Testing Overview and Interview Preparation

This paragraph introduces AB testing as an essential topic for data science interviews, especially for companies like Google, Meta, and Uber. AB testing is used to determine if changes to platforms are effective or due to random chance. The speaker, Dan, outlines seven steps for conducting an AB test, emphasizing the importance of understanding the problem statement, defining hypotheses, designing the experiment, running the test, performing sanity checks, interpreting results, and making a launch decision. The example of an online clothing store, Fashion Web Store, is presented to illustrate the application of AB testing in a real-life scenario.

05:00

🛍️ Defining the User Journey and Success Metric

The paragraph delves into the importance of understanding the user journey for an e-commerce store and how it influences the selection of a success metric for AB testing. It discusses the qualities a good success metric should have, such as being measurable, attributable, sensitive, and timely. The speaker provides a pro tip on preparing for AB testing interviews by outlining the user journey of a product, which aids in designing effective tests. The chosen success metric for the example is revenue per day per user, which aligns with the business goal of increasing sales through a new product recommendation algorithm.

10:02

🎲 Hypothesis Testing and Experiment Design

This section covers the process of setting up hypothesis testing with null and alternative hypotheses for the AB test. It explains the significance level, statistical power, and minimum detectable effect. The design of the experiment is discussed, including determining the randomization unit, targeting the right user population, calculating sample size, and deciding the experiment's duration. The paragraph also advises against peeking at P-values during the experiment to prevent premature conclusions.

15:03

🔍 Running the Experiment and Validity Checks

The paragraph describes the process of running the AB test using instrumentation to collect data and track results. It stresses the importance of not making decisions based on incomplete data and the necessity of conducting validity checks, such as ensuring there are no instrumentation effects, external factors, selection bias, or novelty effects that could skew the results. The speaker also mentions the use of statistical tests like the chi-square test to validate the experiment's design.

20:03

📈 Result Interpretation and Launch Decision

The final paragraph focuses on interpreting the results of the AB test, considering the direction and statistical significance of the success metric, as well as the confidence interval. It presents an example where the new ranking algorithm in the treatment group shows a statistically significant increase in revenue per user compared to the control group. The speaker discusses factors to consider when deciding whether to launch the new algorithm, including metric trade-offs, launch costs, and the risk of a false positive. Various scenarios are explored to illustrate how different results might influence the launch decision.

📧 Conclusion and Further Assistance

In the concluding paragraph, the speaker summarizes the end-to-end process of conducting an AB test and addresses AB testing interview questions. They offer resources for further learning, such as mock interviews, coaching, courses, and community access through their website, datant.tv. The speaker invites viewers to ask questions or reach out via email for additional support.

Mindmap

Keywords

💡AB Testing

AB Testing is a method of comparing two versions of a webpage, product, or service to determine which performs better. In the video, AB testing is the central theme, as it is used to evaluate changes in platforms like Google, Meta, and Uber. The script discusses how data scientists use AB tests to ascertain whether observed changes are due to random chance or the actual implemented changes. An example of testing a new ranking algorithm for an online clothing store is provided to demonstrate the process.

💡Hypothesis Testing

Hypothesis testing is a statistical method used to make decisions about the likelihood of a hypothesis being true. The video describes setting up null and alternative hypotheses for an AB test, which are essential to understanding whether the changes made have a statistically significant effect. The null hypothesis assumes no difference between the control and variant groups, while the alternative hypothesis suggests there is a difference.

💡Success Metric

A success metric is a specific measurement used to determine the success of a product, feature, or strategy. The video emphasizes defining a success metric for an AB test, such as revenue per day per user in the given example. The metric must be measurable, attributable, sensitive, and timely, serving as a proxy for long-term desired behavior.

💡Significance Level

The significance level, often denoted by alpha (α), is the threshold used in hypothesis testing to determine if the results are statistically significant. In the script, a significance level of 0.05 is set, meaning there is a 5% risk of concluding that a difference exists when there is none, commonly known as a Type I error.

💡Statistical Power

Statistical power is the probability that a test will detect a true effect when one exists. The video mentions setting the statistical power at 80%, indicating an 80% chance of correctly rejecting the null hypothesis when it is false, which is a common standard in research.

💡Randomization Unit

The randomization unit refers to the level at which participants are randomly assigned to either the control or treatment group in an experiment. In the video, the randomization unit is the user level, meaning users are randomly assigned to experience either the old or new ranking algorithm.

💡User Journey

User journey describes the path a user takes as they interact with a product or service. The video script discusses the importance of understanding the user journey in the context of an e-commerce store, from visiting the site to searching, browsing, clicking, and eventually purchasing, which is crucial for determining the success metric and target user population for the AB test.

💡Instrumentation

Instrumentation in the context of AB testing refers to the tools and methods used to collect data during an experiment. The video mentions the need for proper instrumentation to track results without bias and to ensure that the data collected is accurate and reliable.

💡Validity Checks

Validity checks are assessments performed to ensure that the results of an experiment are trustworthy and not influenced by extraneous factors. The script describes conducting sanity checks, including checking for instrumentation effects, external factors, selection bias, and novelty effects, before interpreting results and making decisions based on the experiment.

💡P-Value

The P-value is a statistical measure that indicates the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true. In the video, the P-value is used to determine if the observed lift in revenue per day per user is statistically significant, with a P-value of 0.01 indicating strong evidence against the null hypothesis.

💡Confidence Interval

A confidence interval provides a range of values within which the true population parameter is likely to fall, with a certain level of confidence. The video uses a 95% confidence interval to assess the lift in revenue per user, indicating that there is a high level of confidence that the true lift lies between 3.4% and 5.4%.

💡Launch Decision

The launch decision is the choice made after conducting an AB test to determine whether to implement the changes based on the results. The video discusses considering the metric tradeoff, cost of launching, and risk of committing a false positive before making a launch decision, which in the example involves deciding to launch a new algorithm for product recommendations.

Highlights

AB testing is essential for data science interviews, especially for companies like Google, Meta, and Uber.

AB testing helps determine if changes on platforms are due to random chance or actual implemented changes.

The presenter, Dan, a former Google and PayPal data scientist, will provide a walkthrough of an AB test using a real-life example.

There are seven key steps in the AB testing procedure: understanding the problem, defining hypothesis testing, designing the experiment, running the experiment, conducting sanity checks, interpreting results, and making a launch decision.

Understanding the problem involves clarifying the case, identifying success metrics, and understanding the user journey.

Hypothesis testing involves setting up null and alternative hypotheses and determining parameter values like significance level and statistical power.

Experiment design includes determining the randomization unit, target user type, and other considerations.

Running the experiment requires proper instrumentation for data collection and analysis.

Sanity checks are crucial to ensure the experiment's validity and avoid flawed results due to design flaws or biases.

Interpreting results involves analyzing the P-value, considering statistical significance, and understanding the business context.

The decision to launch a change should consider statistical results, business context, and practical significance.

A real-life case study of an online clothing store, Fashion Web Store, is used to illustrate the AB testing process.

The success metric for the case study is revenue per day per user, chosen for its measurability, attributability, sensitivity, and timeliness.

The significance level is set at 0.05, and the statistical power at 80%, with a practical significance of a 1% lift.

Randomization is done at the user level, targeting users who have started searching for products.

Validity checks include ensuring no instrumentation effects, external factors, selection bias, or novelty effects.

The final decision to launch the new algorithm is based on statistical significance, practical significance, and business considerations.

The video concludes with guidance on how to address AB testing interview questions and resources for further learning.

Transcripts

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Related Tags

AB Testing Data Science Interview Prep E-commerce Hypothesis Testing Statistical Power User Journey Success Metric Experiment Design Revenue Optimization