Sample Size

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UX ( 73 )
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Statistics ( 51 )
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Usability ( 32 )
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Rating Scale ( 26 )
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slider ( 2 )
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Cases spike, home prices surge, and stock prices tank: we read headlines like these daily. But what is a spike and how much is a surge? When does something crater versus tank or just fall? Headlines are meant to grab our attention. They often communicate the dramatic story the author wants to tell rather than what the data say. It isn’t easy to write headlines.

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Rating scales have been around for close to a century. It’s no wonder there are many questions about best practices and pitfalls to avoid. And like any topic that’s been around for that long, there are urban legends, partial truths, context-dependent findings, and just plain misconceptions about the “right” and “wrong” way to use and interpret rating scales. We’ve been researching and conducting our own

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Sliders are a type of visual analog scale that can be used with many online survey tools such as our MUIQ platform. The literature on their overall effectiveness is mixed (Roster et al., 2015). On the positive side, evidence indicates that sliders might be more engaging to respondents. On the negative side, evidence also indicates that sliders can be more cognitively and physically challenging than

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Paraphrasing the statistician George Box, all models are wrong, some are useful, and some can be improved. In a recent article, we reviewed the most common way of modeling problem discovery, which is based on a straightforward application of the cumulative binomial probability formula: P(x≥1) = 1 - (1-p)n. Well, it’s straightforward if you like playing around with these sorts of formulas like Jim and

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Finding and fixing problems in an interface is one of the fundamental priorities of a formative usability test. But how many users should you test with? And how many usability problems are there to be uncovered? These questions have been discussed and debated for decades. Early work on problem discovery suggested that the first few users will uncover most of the common problems. This isn’t—or

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The COVID-19 pandemic has led to significant changes in how people have vacationed in 2020. To get away from it all without spending time in crowded places, vacationers have turned to vacation rental websites and have planned longer stays. For example, Airbnb recently reported a year-to-year doubling of long-term (>28 days) rentals and a shift from urban to rural stays. These changes make sense given

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We typically recommend small sample sizes (5–10) for conducting iterative usability testing meant to find and fix problems (formative evaluations). For benchmark or comparative studies, where the focus is on detecting differences or estimating population parameters (summative evaluations), we recommend using larger sample sizes (20–100+). Usability testing can be used to uncover problems and assess the experience. Many usability tests will play both roles simultaneously, formative

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One of the primary goals of measuring the user experience is to see whether design efforts actually make a quantifiable difference over time. A regular benchmark study is a great way to institutionalize the idea of quantifiable differences. Benchmarks are most effective when done at regular intervals (e.g., quarterly or yearly) or after significant design or feature changes. A UX benchmark is something akin to

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We’ve written extensively about how to determine the right sample size for UX studies. There isn’t one sample size that will work for all studies. The optimal sample size is based on the type of study, which can be classified into three groups: Comparison studies: Comparing metrics for statistical differences Standalone studies: Estimates a population metric (such as completion rate or perceived ease) Problem discovery:

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Most methods in UX rely on collecting data (behavioral and attitudinal) from a sample of participants. But knowing how many participants you should use is not a simple question. What's particularly difficult about learning how to compute the right sample size for a study is that books and articles can get overly technical; it's hard to know whether the advice is relevant to an applied

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