The two-sample t-test is one of the most widely used statistical tests, assessing whether mean differences between two samples are statistically significant. It can be used to compare two samples of many UX metrics, such as SUS scores, SEQ scores, and task times. The t-test, like most statistical tests, has certain requirements (assumptions) for its
Cases spike, home prices surge, and stock prices tank: we read headlines like these daily. But what is a spike and how much is a surge? When does something crater versus tank or just fall? Headlines are meant to grab our attention. They often communicate the dramatic story the author wants to tell rather than
It seems like every few years a new standardized UX measure comes along. Standardization of UX measurement is a good thing for researchers and practitioners. Having common methods and definitions helps with objectivity, generalization, economy, and professional communication. At MeasuringU, we pay a lot of attention to the continuing evolution of standardized UX measurement. The
We typically recommend small sample sizes (5–10) for conducting iterative usability testing meant to find and fix problems (formative evaluations). For benchmark or comparative studies, where the focus is on detecting differences or estimating population parameters (summative evaluations), we recommend using larger sample sizes (20–100+). Usability testing can be used to uncover problems and assess the
Decisions should be driven (or at least informed) by data. Raw data is turned into information by ensuring that it is accurate and has been put into a context that promotes good decision-making. The pandemic has brought a plethora of COVID-related data dashboards, which are meant to provide information that helps the public and public
There are a lot of ways to display multipoint rating scales by varying the number of points (e.g., 5, 7, 11) and by labeling or not labeling those points. There’s variety not only in how rating scales are displayed but also in how you score the responses. Two typical scoring methods we discussed earlier are reporting
There is plenty of debate about the best way to quantify attitudes and experiences with rating scales. And among those debates, perhaps the most popular question is the “right” number of response options to use for rating scales. For example, is an eleven-point scale too difficult for people to understand? Is a three-point scale insufficient
There’s a lot of conventional wisdom floating around the Internet about rating scales. What you should and shouldn’t do. Best practices for points, labels, and formats. It can be hard to differentiate between science-backed conclusions and just practitioner preferences. In this article, we’ll answer some of the more common questions that come up about rating
One of these things is not like the other. That’s the theme of a segment on the long-running US TV show Sesame Street. As children, we learn to identify similarities and differences. And after seeing a group of things that look similar, we tend to remember the differences. Why? Well, one theory describes something called
To have a reliable and valid response scale, people need to understand what they’re responding to. A poorly worded question, an unclear response item, or an inadequate response scale can create additional error in measurement. Worse yet, the error may be systematic rather than random, so it would result in unmodeled bias rather than just
