A complete guide to A/B testing

A/B testing, also known as split testing, is a method of comparative analysis used to determine which version of a product change performs better against a specific metric, offering a way to achieve true and measurable results. It involves testing two or more variants with a consistent audience and conditions, allowing any observed changes to be attributed to the modifications being tested. The process includes formulating a hypothesis, choosing relevant metrics, deciding on the number of variants and audience size, selecting a testing duration, preparing a testing dashboard, running the test, and choosing a victor based on data analysis. Best practices for A/B testing emphasize maximizing sample size, monitoring key metrics, avoiding bias, maintaining equal conditions, and documenting the process, while common pitfalls include premature conclusions, overgeneralizing results, poor setup, ignoring statistical significance, and failing to account for external factors. The article highlights the importance of understanding when A/B testing is necessary and recognizing that it is not always required for every update, emphasizing the need for common sense in decision-making.