AI products are forcing UX and product teams to rethink what “good evaluation” means. Traditional software can usually be evaluated as if the same input produces the same output. LLM products do not work that way. The same user can ask the same question twice and receive two different answers. A model can be fluent but wrong, uncertain but useful, or confident in a response that should have been escalated. In this kind of product environment, evaluation cannot rely only on av

There's a lie at the center of most UX reports. A mean SUS score of 74. A task time of 38 seconds. An NPS of +12. These numbers are presented as facts, when really they're estimates - educated guesses about how the whole user population would behave, extrapolated from the 20 or 30 people you managed to get into your study. The problem is what we do next. Most UX practitioners report those point estimates and move on. Maybe a standard deviation if you're feeling generous. Rare

Not all users behave differently in obvious ways. Sometimes they use the same features, spend similar amounts of time in a product, and still have very different goals. That is why behavioral segmentation matters in UX. When teams rely only on averages, they often miss the patterns that explain why some users succeed, some struggle, and some quietly disappear. Behavioral segmentation helps researchers and product teams group users based on what they actually do, not just who

A lot of UX outcomes are not truly continuous. They are often binary, ordered, or categorical. That means standard linear regression is often the wrong tool, even though many researchers still default to it. Logistic regression gives us a better way to model these outcomes because it is built for probabilities and categories rather than assuming a smooth continuous scale. In that sense, It is one of the core tools for turning UX data into defensible product insight. The bas