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
We’ve written a lot about confidence intervals for the last two decades. We especially encourage them for small sample studies. Some of you even bought into our recommendation and use them yourselves (a decision we continue to support). But maybe you’ve heard about Bayesian credible intervals and wonder if you should be using them instead.
“That confirms what I expected.” The same data, two different conclusions. A 90% completion rate from 20 participants on a usability test of a checkout flow. Is that completion rate better than the historical average of 78%? One researcher says yes, definitely. Another says no, it’s in line with the historical average. Both are using
Surveys are a common way to measure attitudes, behaviors, and intentions related to products and services. But large surveys can include dozens of questions and multiple demographic segments, which can mean hundreds of potential comparisons. How do you present all those results in a way stakeholders can quickly scan? You can use a slide deck
It seems like AI is almost everywhere. For many people, it is. From the moment we wake up, AI increasingly shapes our daily experiences. Music playlists are generated automatically. Our computers prompt us to use AI assistants. Internet searches are now often preceded by AI-generated summaries. Call a doctor’s office after hours. and an AI
“Follow the data. Update your beliefs.” We like the idea of applying iterative Bayesian thinking to how we test hypotheses and conduct UX research. The idea is simple, but modern Bayesian math can be opaque and hard to understand. We have questions about how well Bayesian analysis works relative to frequentist analysis. We are also
Say you collect rating scale data from dozens of users across ten apps. To analyze the data, you compute medians because you learned that rating scale data isn’t interval or ratio data. The medians of all ten apps end up the same. They’re all 4! If you rely on the medians, you’d conclude the apps
“That design will never work.” You may have had that thought before you even ran your first participant in a usability test. If you’ve seen enough users struggle and conducted enough usability tests, then you probably have some idea about how well or poorly a task attempt may go for prototypes or even commercially available
The completion rate jumped from 20% to 80%. That’s a large effect size. If it had gone from 20% to 21%? Much smaller effect. It’s easy to get caught up in the mechanics of significance testing and p-values. But even before those tools existed, researchers were measuring effect sizes. Effect sizes remain fundamental to understanding
The UX-Lite® is a relatively new metric, but it is versatile, short, and increasingly popular for UX research. It measures perceived usability and usefulness with just two items. But if you’re using the UX-Lite to compare products or to see whether you’ve improved over time, what sample size do you need? Yes, the sample size
