UX Researcher @ Perceptual User Experience Lab | Human-AI Interaction Researcher @ University of Arkansas at Little Rock
As UX researchers, we often encounter a common challenge: deciding whether one design truly outperforms another. Maybe one version of an interface feels faster or looks cleaner. But how do we know if those differences are meaningful - or just the result of chance? To answer that, we turn to statistical comparisons. When comparing numeric metrics like task time or SUS scores, one of the first decisions is whether you’re working with the same users across both designs or two separate groups. If it's the same users, a paired t-test helps isolate the design effect by removing between-subject variability. For independent groups, a two-sample t-test is appropriate, though it requires more participants to detect small effects due to added variability. Binary outcomes like task success or conversion are another common case. If different users are tested on each version, a two-proportion z-test is suitable. But when the same users attempt tasks under both designs, McNemar’s test allows you to evaluate whether the observed success rates differ in a meaningful way. Task time data in UX is often skewed, which violates assumptions of normality. A good workaround is to log-transform the data before calculating confidence intervals, and then back-transform the results to interpret them on the original scale. It gives you a more reliable estimate of the typical time range without being overly influenced by outliers. Statistical significance is only part of the story. Once you establish that a difference is real, the next question is: how big is the difference? For continuous metrics, Cohen’s d is the most common effect size measure, helping you interpret results beyond p-values. For binary data, metrics like risk difference, risk ratio, and odds ratio offer insight into how much more likely users are to succeed or convert with one design over another. Before interpreting any test results, it’s also important to check a few assumptions: are your groups independent, are the data roughly normal (or corrected for skew), and are variances reasonably equal across groups? Fortunately, most statistical tests are fairly robust, especially when sample sizes are balanced. If you're working in R, I’ve included code in the carousel. This walkthrough follows the frequentist approach to comparing designs. I’ll also be sharing a follow-up soon on how to tackle the same questions using Bayesian methods.