  In applied probability, the normal approximation is often used for the
distribution of data with assumed additive structure. This tradition is based
on the central limit theorem for sums of (independent) random variables.
However, it is practically impossible to check the conditions providing the
validity of the central limit theorem when the observed sample size is limited.
Therefore it is very important to know what the real accuracy of the normal
approximation is in the cases where it is used despite it is theoretically
inapplicable. Moreover, in some situations related with computer simulation, if
the distributions of separate summands in the sum belong to the domain of
attraction of a stable law with characteristic exponent less than two, then the
observed distance between the distribution of the normalized sum and the normal
law first decreases as the number of summands grows and begins to increase only
when the number of summands becomes large enough. In the present paper an
attempt is undertaken to give some theoretical explanation to this effect.
