Norms provide us with such a context. For example, if a person has obtained a raw score of 90 out of 100 for a test of some kind, we may initially think this performance is very good. However, if we compare this score to other people we are interested in and find that they score 95 or higher, it means that the performance of the person we are looking at is in fact low relative to the norm. Further, using raw scores, we cannot identify how a person has performed across a range of tests or questionnaires unless we can get a standard which can be applied to all the tests. Using norms to evaluate performance, we may find that somebody performs strongly using language skills, but is relatively weak in computational skills.
Norms therefore provide a frame of reference with which to view a candidate's performance.
Norms are constructed from a set of scores of candidates that are representative of the population you are measuring or comparing against. A candidate's scores can then be directly compared with the norm group, and the relative performance of the candidate can be identified by how the candidate falls in the distribution of scores on the norm table. In this way one can tell whether a candidate is anything from very poor or low, to average, to exceptional on an ability or characteristic. When assessing a bursary candidate for his or her suitability for university, it is useful to know how the person compares against students who have been successful at that level. By comparing the performance of the candidate on a number of psychometric tests against the norm group of successful students, you can evaluate the likely success of the candidate in obtaining the qualification.