
In A/B, A/B/n, or multivariate testing scenarios, using p-value with traditional null-hypothesis-based statistical analysis is so common, and most designers […]

Traditional A/B testing splits traffic evenly, but multi-armed bandits dynamically send more users to the better-performing version. Here’s how the method works, where it helps, and when UX teams should use it over classic A/B testing.

AI agent simulations promise faster, lower-risk UX testing by replacing real users with AI-simulated personas. Here’s how the method works, where it falls short, and when designers should rely on simulated users versus real user testing.

A/B testing is great for comparing two versions of a design, but multivariate testing helps teams evaluate multiple design element combinations at once. Here’s how both methods work, how they differ, and when UX teams should use each one.