Articles

Kate Moran

Kate Moran is a former NN/G employee who worked on research, teaching, leadership, and design thought leadership. Her expertise spanned both qualitative and quantitative research methods. At NN/G Kate discovered and published ground-breaking findings on artificial intelligence (AI), interaction design, digital content, and information-seeking user behaviors.

Articles and Videos

  • Why Confidence Intervals Matter for UX

    To make valid design decisions from quantitative user research data, you should be familiar with the concept of a confidence interval.

  • Creepiness–Convenience Tradeoff

    As people consider whether to use the new "creepy" technologies, they do a type of cost-benefit analysis weighing the loss of privacy against the benefits they will receive in return.

  • A/B Testing vs. Multivariate Testing for Design Optimization

    Like A/B testing, multivariate testing is a design optimization method that involves experimenting with live traffic to find the best impact on conversions.

  • Children’s Exposure to Digital Technology Causes Parental Anxiety

    North American parents are concerned with technology’s impact on children’s social and emotional development, Chinese parents worry about health and academics

  • Why Users Feel Trapped in Their Devices: The Vortex

    Many users report anxiety and lack of control over the amount of time they spend online. We call this feeling “the Vortex.”

  • Better Link Labels: 4Ss for Encouraging Clicks

    Specific link text sets sincere expectations and fulfills them, and is substantial enough to stand alone while remaining succinct.

  • Designing Search Suggestions

    Useful search suggestions lead to relevant results and are visually distinct from the query text. (This is about how to design the search feature on your own website, whether it's an ecommerce site or not.)

  • Unbridged Knowledge Gaps Hurt UX

    Many websites fail to provide the right information for research-based tasks, requiring unnecessary effort for users to piece together various information sources manually.

  • Statistical Significance in UX

    If you’re working on digital products, you should be familiar with what statistical significance means in the context of UX research. Otherwise, your decisions may be based on meaningless numbers that could be due to pure chance and not a reliable difference between design options.

  • Interpreting Contradictory UX Research Findings

    If your product looks good from one perspective and bad from another, you have to check the methodology and try to interpret the findings.