An organized method for dealing with imprecise data is called fuzzy logic. The data are considered as fuzzy sets. Traditional sets include or do not include an individual element; there is no other case than true or false. Fuzzy sets allow partial membership. An example is "young" people. A ten-year old considers smaller children young. On the other hand, people in the geriatric group may consider those under 70 as young and someone over 90 as old. This means that the assignment of membership in the set of "young people" depends on who is constructing the set and on the purpose of the exercise. For example, we might be setting up a screening procedure in which age is a criterion.
Although fuzzy logic avoids sharp decisions, there is no reason to make the criteria so broad that classification is illogical. In the above case, a reasonable approach would be assigning 15 years or younger as having 100 per cent membership in young people, 40 years or older as zero per cent membership, and ages between 15 and 40 would get partial membership. You could argue about 15 or 40 as the cut offs, but a good or bad fuzzy decision depends on whether it suits a particular problem. There are several possible ways to systematize fuzzy sets, but we will try to follow accepted standards, one of which is letting degree of membership range from 0 to 1.