We do not live in a binary world, yet much of traditional UX research still treats user decisions as if we do. Think about your last product decision. Did you love it or hate it. Of course not. Your actual internal dialogue probably sounded more like this: the onboarding flow was mostly smooth, but the payment screen felt cluttered, the subscription terms made you uneasy, and while the product solved your core problem, you still felt unsure whether you should commit long term

A Practical Guide to Probability Distributions in UX Research Most UX research teams now live in a world of metrics. Conversion rates, task success, time on task, churn, NPS, feature adoption, “rage clicks”, scroll depth. We A/B test them, segment them, and present them in slide decks every week. But under the surface, most of us still treat all of these metrics as if they came from the same simple shape: a nice symmetric bell curve. In the textbook world, variables are conti

You’re walking through a big clothing store, flipping through rack after rack trying to find something interesting. At first everything feels new. Different colors, styles, cuts. After a few minutes, though, you realize you’re seeing almost the same things again and again. Sure, the store is huge, but the variety isn’t unlimited. No matter which aisle you turn into, it’s basically more of what you’ve already seen. At some point you stop searching and think okay, I get it, not

UX research has gone through a quiet revolution. What used to be mostly interviews, usability testing, and expert reviews has expanded into a field deeply driven by data. Today we are not just listening to what users say. We analyze behavioral logs, interaction timing, psychometrics, conversion funnels, eye-tracking traces, error counts, retention curves, and long-term engagement metrics. Our questions have also become sharper. We no longer ask simply which design people like

Most UX research still treats user experience as a series of snapshots. We run a usability test, collect a System Usability Scale score, send a Net Promoter Score wave, or run an intercept survey at the end of a flow. These are useful for diagnosing immediate friction and capturing static sentiment, but they miss something fundamental about how humans interact with products over time. User attitudes are not fixed states. They are trajectories. Trust builds, then cracks. Frust

“Nothing is more difficult, and therefore more precious, than to be able to decide.” – Napoleon Bonaparte Well, lucky him. He never had to survive the modern tyranny of too many choices. Back then, a decision didn’t involve comparing 27 apps just to set an alarm. Today, every little task comes with endless options, all demanding attention, evaluation, and mental energy. We pick products, tools, workflows, and features while quietly negotiating time, effort, risk, frustration,

Instead of guessing what users are thinking, UX research can quantify the moment-to-moment effort a person’s brain expends while navigating digital systems. That effort is called cognitive workload, and it reflects how much of a user’s limited mental capacity is being consumed to perform a task. Understanding cognitive load matters because poor design can overwhelm users without ever showing up in usability ratings or performance data. A user might technically finish a workfl

Product teams, UX researchers, and marketers often face a frustrating paradox, they seek customer input to make better decisions, but the feedback they receive makes everything look equally important. On surveys, users rate most features as valuable. They approve nearly every message. They feel that multiple benefits are essential. This sounds helpful at first, yet it becomes a roadblock when teams must decide what to build, what to highlight, and what to ignore. The problem

Imagine working on the home screen redesign of a major streaming app. Designers insist that a cinematic hero banner will make the experience feel premium. Product managers push for more personalized rows to improve engagement. Leadership argues that a dedicated ad space could unlock new revenue. You run interviews, surveys, and usability tests to untangle these opinions, and users repeat a familiar phrase: “Well, it depends...” It depends on what they get in exchange. A bold

For decades, digital product and service design focused on quantifiable performance metrics: clicks, errors, task completion rates, and time on task. These numbers were easy to capture and simple to benchmark, which made them ideal for early UX and HCI work. But as our understanding of human behavior grew, it became clear that these metrics reveal only the surface of the user experience. They tell us what people did, not why they did it or how they felt while doing it, a patt

Understanding how people think, feel, and perform while interacting with systems requires methods that can capture the brain and body in real time without disrupting the task itself. In Human Factors (HF) and User Experience (UX) research, tools like Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), eye tracking, and Galvanic Skin Response (GSR) provide powerful ways to study attention, emotion, and cognitive load as users engage with technology. Ha

Qualitative user experience (UX) research is undergoing a profound evolution, catalyzed by the emergence of advanced artificial intelligence (AI) tools. For decades, qualitative UX has relied on traditional methods such as interviews, usability testing, think-aloud protocols, and open-ended surveys. These approaches are rich in user insight but notoriously time-intensive and constrained by scalability. With AI, researchers now have the ability to accelerate processes, surface

Over the last year, I’ve seen “AI-powered personas” that were really just LLMs role-playing as UX researchers. We’d prompt them to “create three personas for a banking app,” and get back “Frugal Fiona,” “Investor Ivan,” and “Tech Averse Tom.” It looks impressive for five seconds until you realize it’s just polished guessing. That’s not using AI. That’s outsourcing imagination. The real value of AI in UX isn’t about inventing users. It’s about discovering them. As a cognitive

Artificial Intelligence (specifically Machine Learning) has opened new possibilities for understanding user behavior. Yet, its value in UX research depends entirely on how and when it is applied. As researchers seeking to uncover why people behave the way they do, we often rely on summarizing performance through simple aggregates: average task completion time, average satisfaction scores, or average conversion rates. However, the so-called “average user” is largely a conveni

When UX researchers hear “Structural Equation Modeling,” it often sounds like something reserved for academic journals or data scientists with specialized training. However, SEM is one of the most valuable frameworks for understanding how complex UX systems actually work. UX researchers rarely stop at asking what is happening. They want to know why and how. SEM is designed for precisely that. It is most useful when survey or telemetry data appear too messy for simple averages

Duration: 5 sessions × 2 hours (10 hours total) Audience: UX designers and researchers Instructor: Mohsen Rafiei, Ph.D. Format: Online, hands-on sessions using simple tools (Jamovi, JASP, R). Registration Fee for all 5 sessions: $850 Email : Admin@Puxlab.com The Quantitative UX (From Basics to Modeling) course takes UX researchers, and designers on a full learning journey, from the very basics of quantitative thinking to advanced analytical methods used in modern researc

دکتر محسن رفیعی استاد روان‌شناسی دانشگاه آرکانزاس و بنیان‌گذار آزمایشگاه تجربهٔ ادراکی کاربر است. او پس از دریافت دکترای روانشناسی...