A/B 테스팅 테크니컬 인터뷰 전 정리해 두어야 할 사항 및 FAQ

 

 

 

안녕하세요! 오늘은 A/B 테스트에 대해 이야기하려고 합니다. A/B 테스트는 새로운 제품이나 기능을 출시하기 전에 사용자들의 반응을 예측하기 위해 널리 활용되는 실험 방법입니다. 간단히 말하면, 두 가지 이상의 변형을 만들어 이를 무작위로 나누어 사용자들에게 제공하고, 각 변형의 성과를 비교하여 가장 우수한 결과를 내는 변형을 선택하는 방법입니다. 이 방법은 데이터 과학 분야에서 핵심 역량 중 하나이며, 많은 데이터 과학자들이 자주 접하게 되는 주제 중 하나입니다. 

오늘 이 포스트에서는 A/B 테스팅 테크니컬 인터뷰에서 나올 수 있는 내용들에 대해서 요약해 보는 시간을 가져보고자 합니다. 미국 데이터 사이언티스트 인터뷰를 주 컨텐츠로 하는 만큼 내용은 영어로 정리해 보았습니다.

 

1. What is A/B Testing?

A/B testing, also known as controlled experiments, is widely used in industry to make product launch decisions. In the simplest form, there are two variants: control group A and treatment group B. Typically, the control group uses the existing feature while the treatment group uses the new feature. A/B testing allows tech companies to evaluate a feature with a subset of users to infer how it may be received by all users.

 

2. How Long to Run an A/B Test?

One commonly asked question during interviews is how long to run an A/B test. To decide the duration of the test, we need to obtain the sample size, and three parameters are needed to get it: type 2 error or power, significance level, and the minimum detectable effect. The sample size can be obtained using the rule of thumb formula. Once we know the sample size, we could obtain the number of days to run the experiment by dividing the sample size by the number of users in each group.

 

3. Multiple Testing Problem

Sometimes we run tests with multiple variants to see which one is the best amongst all the features. This can happen when we want to test multiple colors of a button or test different home pages. In such a scenario, the probability of false discoveries increases, and this is called the multiple testing problem. Bonferroni correction is one commonly used method to deal with this problem.

 

4. Novelty and Primary Effect

When there is a change in the product, people react to it differently. Some are used to the way it works and are reluctant to change. This is called primary effect or change aversion. Others may welcome changes, and the new feature attracts them to use more. This is called the novelty effect. Both effects will not last long, and people's behavior will stabilize after a certain amount of time.

 

5. Interference Between Variants

Interference between control and treatment groups can also lead to unreliable results. In the ideal scenario, each user is independent, and we expect no interference between control and treatment groups. However, sometimes this does not work. This may happen for testing social networks such as Facebook or two-sided markets such as Uber, Lyft.

 

6. Dealing with Interference

To deal with interference between control and treatment groups, we need to isolate users in the control and treatment group. For two-sided markets, we could use geo-based randomization or time-based randomization. For social networks, one way is to create network clusters to represent groups of users who are more likely to interact with people within the group than people outside of the group. Another way is called ego network randomization.

 

 

오늘은 A/B 테스트에 대해 배워보았습니다. A/B 테스트는 제품 런칭 전 중요한 단계이며, 올바른 방법으로 설계하고 분석하면 제품 개발 및 마케팅 전략 수립에 많은 도움이 됩니다. A/B 테스트를 수행할 때에는 각 요소들이 성과에 어떠한 영향을 미치는지를 이해하고, 테스트 결과를 정확하게 분석하여 신뢰성 있는 결론을 도출하는 것이 중요합니다. 더 나아가, A/B 테스트를 통해 얻은 인사이트를 바탕으로 지속적인 개선과 실험을 진행하여 더 나은 제품과 서비스를 제공할 수 있도록 노력해야 합니다.

 

➡️ 미국 현직 데이터 사이언티스트와 함께 미국 데이터 관련 대학원 진학을 준비해 보세요: https://www.datakorlab.com/p/1-11

Graduate Consulting

 

➡️ 미국 현직 데이터 사이언티스트와 함께 취업을 준비해 보세요: https://www.datakorlab.com/p/1-1-100

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