The Net Promoter Score (NPS) is a popular business metric used to track customer loyalty. It uses a single likelihood-to-recommend (LTR) question (“How likely is it that you will recommend our company to a friend or colleague?”) with 11 scale steps from 0 (Not at all likely) to 10 (Extremely likely). In NPS terminology, respondents
The Net Promoter Score (NPS) is a widely used metric, but it can be tricky to work with statistically. One of the first statistical steps we recommend that researchers take is to add confidence intervals around their metrics. Confidence intervals provide a good visualization of how precise estimates from samples are. They are particularly helpful
During the fall in the northern hemisphere, leaves change colors, birds fly south, and the temperature gets colder. Do the birds change the color of the leaves, and does their departure make the temperature colder? What if you gave participants two versions of a rating scale, with the first having responses ordered from strongly disagree
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
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
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
What sample size do we need? It’s consistently among the most common questions I get from researchers. It can be a confusing process, but that’s why we cover sample-size planning at the Denver UX Boot Camp. Determining the right sample size for a project is a science–an imprecise science. It’s like appraising a house: you
Seeing is believing. Observing just a handful of users interact with a product can be more influential than reading pages of a professionally done report or polished presentation. But what if a stakeholder only has time to watch two or just one of the users in a usability study? Are there circumstances where watching some
There are a lot of misconceptions about when it is and when it is not appropriate to test with five users. There’s no reason to take an extreme position on this issue and think it’s never acceptable or always the right number. Instead you should understand what you can and cannot learn from just a
Some people think that if you have a small sample size you can’t use statistics. Put simply, this is wrong, but it’s a common misconception. There are appropriate statistical methods to deal with small sample sizes. Although one researcher’s “small” is another’s large, when I refer to small sample sizes I mean studies that have
