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A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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Let’s say you work at Meta (Facebook).

There is currently a “people you may know feature” on Facebook that recommends people a user might want to add as a friend.

However, internal research shows that users with more friends tend to make fewer posts.

How would you approach tackling this issue?

How would you improve the "people you may know" feature?

Friends vs Posts

Business Problem Solving

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Question	Topic	Difficulty	Ask Chance
Friends vs Posts	Business Problem Solving	Hard	Very High
Boarding Times Bias	Statistics	Medium	High
Hundreds of Hypotheses	Statistics	Medium	High

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