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The most used dissimilarity coefficients in numerical taxonomy are those corresponding to different taxonomical distances. In the present paper we performed a review about the distance c2. This distance has advantages over the Average or Euclidian taxonomical distance, because it allows to perform non-exact hypothesis testing to determine existing statistical equality between pairs of operative taxonomical units obtained from the probabilistic distribution of c2 with one degree of freedom, and it uses the significance level (a) as a similitude measure. The cutting height for a dendrogram, obtained in a cluster analysis based on this matrix can be determined; thus, the researcher, when choosing the desired a level for placing groups together, does not need to calculate other dendrogram partitioning tests such as the cubic clustering criterion or Hottelling’s pseudo statistic t2. To illustrate the advantages of the c2 distance, compared to the Euclidian and Manhattan distances, we show an example with hawthorn genotypes (Crataegus spp.) using cluster analysis validated with canonical discriminant analysis. We found that the c2 distance was the best option.

CONSTRUCCIÓN DE DENDROGRAMAS DE TAXONOMÍA NUMÉRICA MEDIANTE EL COEFICIENTE DE DISTANCIA c2: UNA REVISIÓN