To conduct our evaluation we used nine measures of distinctiveness implemented in our Python package <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">pydistinto</hi>. They can be classified into three groups: frequency-based, distribution-based, and primarily dispersion-based measures. Frequency-based measures, including the Ratio of relative frequencies (RRF), chi-square test, and Log-likelihood ratio test (LLR), focus on word frequency, ignoring distribution and dispersion. Distribution-based measures, like Welch’s t-test, assess deviations from the central tendency within each text group. Dispersion-based measures, including Burrows Zeta, logarithmic Zeta, Eta, our implementation of TF-IDF-based measure, and Wilcoxon rank-sum test, evaluate how evenly a word is distributed across each text group.