Statistics with Non-Precise Data

Abstract

In statistical analysis data are usually assumed to be numbers or vectors. But real measurement data of continuous quantities are not precise numbers but more or less non-precise. This imprecision is different from measurement errors and is also called fuzziness. Before analysing the data it is necessary to describe the imprecision of measurements quantitatively. This can be done using the concepts of fuzzy numbers and fuzzy vectors. Then stati stical inference procedures are generalized to the more realistic situation of non-precise data.