  The decision whether a measured distribution complies with an
equidistribution is a central element of many biostatistical methods. High
throughput differential expression measurements, for instance, necessitate to
judge possible over-representation of genes. The reliability of this judgement,
however, is strongly affected when rarely expressed genes are pooled. We
propose a method that can be applied to frequency ranked distributions and that
yields a simple but efficient criterion to assess the hypothesis of
equiprobable expression levels. By applying our technique to surrogate data we
exemplify how the decision criterion can differentiate between a true
equidistribution and a triangular distribution. The distinction succeeds even
for small sample sizes where standard tests of significance (e.g. chi^2) fail.
Our method will have a major impact on several problems of computational
biology where rare events baffle a reliable assessment of frequency
distributions.
