2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/30660Statistical NLP systems are frequently evaluated and compared on the basis of their performances on a single split of training and test data. Results obtained using a single split are, however, subject to sampling noise. In this paper we argue in favour of reporting a distribution of performance figures, obtained by resampling the training data, rather than a single number. The additional information from distributions can be used to make statistically quantified statements about differences across parameter settings, systems, and corpora.To be presented in ACL/EACL Workshop on Evaluation for Language and Dialogue SystemsComputation and LanguageG.3; I.2.6; I.2.7text