If you know even just a little about statistics, you know that the value .05 is special. When the p-value obtained from conducting a statistical test falls below .05, it typically gets a special designation we call statistically significant. This is the conventional threshold for publishing findings in academic journals, and consequently, it is ascribed
For the past few years, we’ve written extensively about our research and usage of the UMUX-Lite. That research has followed the increase in popularity of this compact questionnaire. From its initial publication in 2013, the UMUX-Lite (Usability Metric for User Experience—Lite Version) has become an increasingly popular measure of perceived usability. Figure 1 shows the
The UMUX-Lite is a popular two-item measure of perceived usability that combines perceived ratings of Ease and Usefulness, as shown in Figure 1. Figure 1: Standard version of the UMUX-Lite (standard item wording with five-point scales). Since we began regularly using the UMUX-Lite in our practice, we’ve had numerous clients ask whether it
The original wording of the UMUX-Lite Perceived Usefulness item is “{Product}’s capabilities meet my requirements.” Since we started using the UMUX-Lite in our practice, we’ve had numerous clients ask whether it would be possible to simplify the wording of this item to more closely match the simplicity of the UMUX-Lite Perceived Ease item, “{Product} is
Sample size estimation is a critical step in research planning, including when you’re trying to detect differences in measures like Net Promoter Scores. Too small of a sample and you risk not being able to differentiate real differences from sampling error. Too large of a sample and you risk wasting resources—researchers’ and respondents’ time and,
Hypothesis testing is one of the most common frameworks for making decisions with data in both scientific and industrial contexts. But this statistical framework, formally called Null Hypothesis Statistics Testing (NHST), can be confusing (and controversial). In an earlier article, we showed how to use the core framework of statistical hypothesis testing: you start with
Making decisions with data inevitably means working with statistics and one of its most common frameworks: Null Hypothesis Significance Testing (NHST). Hypothesis testing can be confusing (and controversial), so in an earlier article we introduced the core framework of statistical hypothesis testing in four steps: Define the null hypothesis (H0). This is the hypothesis that
Statistically significant. p-value. Hypothesis. These terms are not only commonly used in statistics but also have made their way into the vernacular. Making sense of most scientific publications, which can have practical, significant effects on public policy and your life, means understanding a core framework with which we derive much knowledge. That framework is called
Understanding who your users are and what they think about an experience is an essential step for measuring and improving the user experience. Part of understanding your users is understanding how they are similar and different with respect to demographics, psychographics, and behaviors. These groupings are often called clusters or segments to refer to the
Researchers rely heavily on sampling. It’s rarely possible, or even makes sense, to measure every single person of a population (all customers, all prospects, all homeowners, etc.). But when you use a sample, the average value you observe (e.g. a completion rate, average satisfaction) differs from the actual population average. Consequently, the differences between designs
