What is A/B testing, and how can it be used to improve Wikipedia articles?
A/B testing, also known as split testing, is a method of comparing two versions of something to determine which one performs better. In the context of Wikipedia articles, it involves creating two slightly different versions of a section, sentence, image, or other element and then tracking which version leads to better user engagement or understanding. This is generally difficult and often impractical on Wikipedia due to the collaborative nature of editing and the lack of built-in A/B testing tools. Hypothetically, to use A/B testing, one might subtly alter the wording of a paragraph in one version of an article and leave the original wording in another 'version' (perhaps a draft in a user's sandbox), and then observe (informally, as precise tracking is unavailable) whether readers interact differently with each. User interaction could be measured through indirect means, like observing if editors spend more time editing a section associated with one phrasing over another, or if one phrasing leads to more questions or clarifications on the article's talk page. If one version consistently leads to improved engagement or understanding (fewer edits for clarification, more positive feedback on the talk page), it suggests that this version is more effective. Note, however, that performing rigorous A/B testing on Wikipedia is not standard practice, and such changes must always be made in accordance with Wikipedia's core policies and guidelines, including maintaining a neutral point of view and relying on reliable sources. The collaborative editing environment and lack of formal testing tools make controlled experimentation challenging.