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Category: ab-testing

  • A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play

    A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play What a simple puzzle game reveals about experimentation, product thinking, and data science The post A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play appeared first on Towards Data Science. Yu Dong Go to original source

  • Critical Mistakes Companies Make When Integrating AI/ML into Their Processes

    Critical Mistakes Companies Make When Integrating AI/ML into Their Processes What I’ve learned leading AI teams across industries The post Critical Mistakes Companies Make When Integrating AI/ML into Their Processes appeared first on Towards Data Science. Andrey Chubin Go to original source

  • Experiments Illustrated: Can $1 Change Behavior More Than $100?

    Experiments Illustrated: Can $1 Change Behavior More Than $100? I currently lead a small data team at a small tech company. With everything small, we have a lot of autonomy over what, when, and how we run experiments. In this series, I’m opening the vault from our years of experimenting, each story highlighting a key…

  • One-Tailed Vs. Two-Tailed Tests

    One-Tailed Vs. Two-Tailed Tests Introduction If you’ve ever analyzed data using built-in t-test functions, such as those in R or SciPy, here’s a question for you: have you ever adjusted the default setting for the alternative hypothesis? If your answer is no—or if you’re not even sure what this means—then this blog post is for…

  • Four Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh)

    Four Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh) In A/B testing, you often have to balance statistical power and how long the test takes. Learn how Allocation, Effect Size, CUPED & Binarization can help you. Image by author In A/B testing, you often have to balance statistical power and how long…

  • Bayesian A/B Testing Falls Short

    Bayesian A/B Testing Falls Short Why Bayesian A/B testing can lead to misunderstandings, inflated false positive rates, introduce bias and complicate results (Image generated by the author using Midjourney) Over the past decade, I’ve engaged in countless discussions about Bayesian A/B testing versus Frequentist A/B testing. In nearly every conversation, I’ve maintained the same viewpoint:…

  • Synthetic Control Sample for Before and After A/B Test

    Synthetic Control Sample for Before and After A/B Test Learn a simple way to use linear regression to create a synthetic control sample for your A/B test Continue reading on Towards Data Science » Gustavo R Santos Go to original source