The term completeness is used regarding the limits of an observation's or survey's detections: for a given apparent magnitude, a survey's completeness is the fraction of objects of that magnitude that are actually detected, the others presumably lost by noise, such as that inherent in the instrument. For example, one might say for a particular survey that for magnitude 20, its completeness is 95%. Generally, the larger the magnitude (i.e., the fainter the object appears in the sky), the smaller the completeness fraction. The general issue stems from the desire to get and use absolutely every bit valid information possible up to the limits of the instrument.
A completeness limit for a survey or a dataset derived from it, and for a given limiting fraction, is the magnitude at which completeness has fallen to that fraction. One could describe a dataset as having a 95% completeness limit of magnitude 20, meaning that at least 95% of any object with magnitude smaller than 20 is included.
Knowing a survey's completeness depends upon knowing what the survey hasn't seen, so it is estimated. If data from a survey with more sensitivity is available, that can be used. Otherwise, the general approach is to create mock data based upon the distribution seen at closer distances, assuming some uniformity across time and space, and to calculate what is likely to be out there.
Another issue for surveys, termed contamination is sufficient noise to appear like an object where there actually is none. The ratio of such occurrences to real objects is termed its purity (at a given magnitude) and analogous purity limits can be estimated for a specific survey.