AI can “watch” videos. It can even generate a list of problems. In some cases, these problem lists seem to be reasonably consistent (reliable). But consistency is not accuracy. Are these real problems or just sophisticated AI slop generated consistently by autocorrect for video? How can we know? One way to find out is to
It looks like AI can “watch” videos. And if AI can watch videos, it can likely extract UI problems. That suggests it has the potential to support UX research. So maybe AI can “watch” a video and detect some problems. But if you run the same video through AI multiple times, do you get the
You may have become numb to the overhyped headlines about AI. But it’d be wrong to dismiss the impact AI can have on our industry, not only because of job displacement, but also of helping us do our jobs more effectively (hopefully). To separate the hype and hysteria, we at MeasuringU think about AI’s impact
“What sample size do I need?” We’ve all been trained from years of math education to expect a single answer to that question—a single sample size number. But earlier, we warned against the quixotic quest to identify the one true sample size to use for UX research—the “magic number.” Because sampling error is real but
What sample size should you use for a problem discovery (formative) usability study? In practice, the answer is based on both statistics AND logistics. A statistical formula will tell you an optimal number to select. But the real-world logistical constraints of budgets, recruiting challenges, and time will often dictate the maximum number of participants you
“How many participants should you run in a usability study?” How many times have you heard that question? How many different answers have you heard? After you sift through the non-helpful ones, probably the most common answer you’ve heard is five. You might have also heard that these “magic 5” users can uncover 85% of
In Think Aloud (TA) testing, participants speak their thoughts while attempting tasks. The process is meant to help researchers identify usability problems and potential fixes. Indeed, in an earlier analysis, we found an increase in problem discovery. Our evaluation of 153 videos, split between TA and non-TA, revealed that evaluators uncovered 36–50% more problems with
One of the most popular UX research methods is Think Aloud (TA) usability testing. In TA testing, participants speak their thoughts while attempting tasks. The process helps researchers identify usability problems and potential fixes. But is the process of thinking aloud necessary to uncover problems and insights? Earlier, we investigated the effects of TA on
In an earlier article, we described the most common methods for modeling the total number of unique usability problems uncovered in a usability test: the average problem occurrence (p), adjusted problem occurrence (adj-p), beta-binomial, and specific problem probabilities. While these methods provide reasonably accurate predictions of the total number of unique problems, there is still
We’ve written extensively about question types, the elements of good and bad writing, why people forget, and common problems with survey questions. But how do you get started writing questions? Few professionals we know have taken a formal course in survey development and instead rely on their experiences or best practices. Despite being called questions,
