![]() The idea behind A/B testing is that you show the variated version of the product to a sample of customers (the experimental group) and the existing version of the product to another sample of customers (the control group). However, if you want to learn or refresh your knowledge in the essential statistical concepts you can check this article: Fundamentals of statistics for Data Scientists and Data Analysts If you have no prior Statistical knowledge, you can simply skip the statistical derivations and formulas. This article is dedicated to both technical and non-technical audiences where I will cover the following topics that one should consider and conduct when performing an A/B test: - What is A/B testing and when to use it? - Questions to clarify before any A/B test - Choice of Primary metric - Hypothesis of the test - Design of the test (Power Analysis) - Calculation of Sample Size, Test Duration - Statistical tests (T-test, Z-test, Chi-squared test) - Analysing A/B test results in Python - Bootstrapping and Bootstrap Quantile Method for SE and CI - Statistical Significance vs Practical Significance - Quality of A/B test (Reliability, Validity, Potency) - Common problems and pitfalls of A/B testing - Ethics and privacy in A/B testing ![]() A/B testing also called split testing, originated from the randomized control trials in Statistics, is one of the most popular ways for Businesses to test new UX features, new versions of a product, or an algorithm to decide whether your business should launch that new product/feature or not. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |