The standard deviation is the most common way of measuring variability or “dispersion” in data. The more the data is dispersed, the more measures such as the mean will fluctuate from sample to sample. That means higher variability (higher standard deviations) requires larger sample sizes. But exactly how much do standard deviations—whether large or small—impact
Are customers more satisfied this quarter than last quarter? Do users trust the brand less this year than last year? Did the product changes result in more customers renewing their subscriptions? When UX researchers want to measure attitudes and intentions, they often ask respondents to complete multipoint rating scale items, which are then compared with
What is the impact if you sample a lot of your population in a survey? Many statistical calculations—for example, confidence intervals, statistical comparisons (e.g., the two-sample t-test), and their sample size estimates—assume that your sample is a tiny fraction of your population. But what if you have a relatively modest population size (e.g., IT decision-makers
Near the top of the list of concerns people have when using statistics with UX data is what to do with non-normal data. If you remember only a few things from statistics class, you might recall something about data needing to look like the infamous bell curve; more specifically, it needs to be normally distributed.
If you took an intro to stats class (or if you know just enough to be dangerous), you probably recall two things: something about Mark Twain’s “lies, damned lies …,” and that your data needs to be normally distributed. Turns out both are only partly true. Mark Twain did write the famous quote, but he
Statistics doesn’t have a Magna Carta, constitution, or bill of rights to enumerate laws, guiding principles, or limits of power. There have been attempts to articulate credos for statistical practice. Two of the most enduring ones are based on the work by Robert P. Abelson, a former statistical professor at Yale. If Abelson wasn’t the
The System Usability Scale (SUS) has been used in industrial user experience research since the mid-1980s. Despite its age, the SUS is still a popular measure, widely used in benchmark tests of software products to measure perceived usability. One reason for its popularity is the extent to which its measurement properties have been comprehensively studied
When learning statistics, you’ll encounter many formulas based on principles of probability and mathematics. But statistics isn’t just a formulaic process where you enter data and are told what to do. Statistics should guide, not dictate, decisions. In making decisions, though, there are different styles of interpreting data. Although a lot of people think statistics
Surveys are one of the most popular methods in applied research. While many have argued that surveys are overused, it’s hard to believe that surveys have no place in multi-method UX research. When conducting survey-based research, you’ll often encounter the terms census, poll, and questionnaire used in conjunction with—and often interchangeably with—the term survey. But
In a previous article, we described three ways to estimate SUS scores from UX-Lite™ (and UMUX-Lite) scores, using either both items (perceived measures of Ease and Usefulness) or the Ease item only: Two-item interpolation: Scaling the mean of both items to a 100-point scale (Lite). One-item interpolation: Scaling just the Ease item to a 100-point
