One of the most apparent trends in test research and application in recent years has been the increasing application of statistical methods and machine learning. The trend has been driven by factors such as increases in data volume, need to detect subtle defects and need to extend beyond test's traditional sort function to a feedback-providing, yield-learning role. These factors, in turn, reflect needs for efficient data handling, ability to extract small signals from noise, and practically-useful model-building. Similar needs exist in other fields of emerging interest including unstructured text analysis, which in turn supports myriad applications. This paper surveys analysis techniques that have been used in the test domain, draws parallels in unstructured text analysis and suggests insights similar to those used to meet recent test challenges can find application in diverse and emerging fields as well. © 2014 IEEE.