  Statistical convergence was introduced in connection with problems of series
summation. The main idea of the statistical convergence of a sequence l is that
the majority of elements from l converge and we do not care what is going on
with other elements. We show (Section 2) that being mathematically formalized
the concept of statistical convergence is directly connected to convergence of
such statistical characteristics as the mean and standard deviation. At the
same time, it known that sequences that come from real life sources, such as
measurement and computation, do not allow, in a general case, to test whether
they converge or statistically converge in the strict mathematical sense. To
overcome limitations induced by vagueness and uncertainty of real life data,
neoclassical analysis has been developed. It extends the scope and results of
the classical mathematical analysis by applying fuzzy logic to conventional
mathematical objects, such as functions, sequences, and series. The goal of
this work is the further development of neoclassical analysis. This allows us
to reflect and model vagueness and uncertainty of our knowledge, which results
from imprecision of measurement and inaccuracy of computation. In the context
on the theory of fuzzy limits, we develop the structure of statistical fuzzy
convergence and study its properties.
