Practitioner’s Guide to Statistical Tests
<p>Hi, we are <a href="https://www.linkedin.com/in/marnikitta" rel="noopener ugc nofollow" target="_blank">Nikita</a> and <a href="https://www.linkedin.com/in/daniel-savenkov-098b41149" rel="noopener ugc nofollow" target="_blank">Daniel</a> from the CoreML team at VK. It’s our job to design and improve recommender systems for friends, music, videos and the news feed. This involves tons of A/B tests, and our progress at the end of the day relies heavily on how accurate and efficient our A/B tests were.</p>
<p>The two most essential things in A/B tests are the design of the experiments and accurate analysis of the experiments’ results. In this article, we will stick to the most common design and compare various statistical analysis procedures, from the very standard t-test and Mann-Whitney test to state-of-the-art approaches like the reweighted bootstrap. The code to reproduce everything you will see in this post is available here:</p>
<p><a href="https://vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f"><strong>Click Here</strong></a></p>