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
Advice on rating scale construction is ubiquitous on the internet and in the halls of organizations worldwide. The problem is that much of the advice is based not on solid data but rather on conventional wisdom and what’s merely thought to work. Even published papers and books on survey design can present a perspective that
When we conduct a survey, we want the truth, even if we can’t handle it. But standing in the way of our dreams of efficiently collected data revealing the unvarnished truth about customers, prospects, and users are the four horsemen of survey errors. Even a well-thought-out survey will have to deal with the inevitable challenge
Errors can provide a lot of diagnostic information about the root causes of UI problems and the impact such problems have on the user experience. The frequency of errors—even trivial ones—also provides a quantitative description of the performance of a task. The process of observing and coding errors is more time-consuming and dependent on researcher
Errors happen and unintended actions are inevitable. They are a common occurrence in usability tests and are the result of problems in an interface and imperfect human actions. It is valuable to have some idea about what these are, how frequently they occur, and how severe their impact is. First, what is an error? Slips
