  The article contains a methodology for social statistics assessing. The
significance of minorities (groups that differ in their attributes from the
majority) has grown substantially in the modern postindustrial economy and
society. In the multidimensional characteristics space distribution analysis
subjects that not included in the sample can be negligible, but they may be
metrically significant. For example, they can be located compactly and remotely
from the main mass of subjects, i.e. have similar characteristics and
significantly influence the dynamics of socio-economic systems. In addition, it
is necessary to evaluate not the probability of errors in each characteristic
separately, but the probability of errors in their combinations. The projection
of a multidimensional space into two-dimensional or three-dimensional space,
usually used to analyze such data, leads to the loss of information about
compact minorities that merge into a "false majority" or the appearance of
distribution. Thus, the posed task can not be reliably solved without analysis
in a multidimensional space. This problem is especially relevant in states
aggregated social statistics analysis for which relatively small number of
subjects (countries) are represented. The aim of the article is to develop
tools for the metric analysis of aggregated social statistics, which is
characterized by a small number of measurements. It is developed to check the
existence of stable metric and topology structures (clusters) and distribution
of countries based on UN data on countries social development characteristics.
