Caleb Sponheim Ph.D. is a User Experience Specialist with Nielsen Norman Group. A former computational neuroscientist, his expertise includes quantitative user experience research, statistics, analytics, and data science.
A strong AI strategy is built by answering those three essential questions honestly. What is our core business? Are we chasing real value or just perception? And what specific problem are we actually trying to solve?
Hype happens when messaging becomes divorced from good experiences. Learning from the virtual reality hype cycle can help us avoid future distractions.
Common UX metrics can be grouped into three types: continuous, discrete, and binary. Continuous metrics occur on a smooth spectrum, like time. Discrete metrics have regular intervals between them, such as satisfaction score. Binary metrics have two possible values, like pass or fail.
Statistical significance does not always equal practical significance. A difference may be statistically significant without having any meaningful impact in real life. Conversely, practical differences may not always achieve statistical significance.
Manage your AI anxiety with two quick tips: small experiments and a news diet. Experiment with day-to-day communication and drafting with AI, as integrating AI into your research process. To avoid news overwhelm, subscribe to three weekly sources of information: a newsletter, an optimistic writing source, and a skeptical writing source.
Our research and evaluation show that there are currently few design-specific AI tools that meaningfully enhance UX design workflows. As of Spring 2024, AI isn’t ready for designers to take advantage of them.
Presenting study data to stakeholders is a crucial step of most projects. Make sure to present your data truthfully and responsibly to avoid costly negative outcomes and reputation loss. Indicate who your data represents and communicate the limitations of your findings.
