  This paper presents an extensive survey of regular distributions in natural
and social sciences. The survey includes studies from a wide scope of academic
disciplines, in order to create an inventory of the different mathematical
functions used to describe the distributions. The goal of the study was to
determine, whether a unique function can be used to describe all the
distributions (universality) or a particular function is best suited to
describe the distributions in each specific field of research (domain
universality). We propose a classification of distributions into eighth
different categories, based on three representations: the Zipf representation,
the cumulative density function (CDF) and the probability density function
(PDF). In the 89 cases included in the survey, neither universality nor domain
universality was found. In particular, based on the results of the survey, the
claim that "power law provides a good description for majority of
distributions" may be rejected. Only one third of the distributions in our
survey are associated with power laws, while another third is well described by
lognormal and similar functions (Dagum, Weibull, loglogistic and Gamma
functions). We suggest that correct characterization of a distribution relies
on two conditions. First, it is important to include the full range of the
available data to avoid distortion due to arbitrary cut off values. Second, it
is advisable to display the data in all three representations: the Zipf
representation, the CDF and the PDF.
