<head xmlns="http://www.tei-c.org/ns/1.0">Hypotheses</head> <list xmlns="http://www.tei-c.org/ns/1.0" type="ordered"> <item>We hypothesize that frequency-based measures (RRF, LLR, and chi-square tests) will show high variations in distinctiveness scores even when the frequency difference of an artificial word between the target and comparison corpus is relatively small. This assumption stems from the statistical nature of these measures, which treat a corpus as a bag of words and do not account for word dispersion. </item> <item>When the frequency of an artificial word is the same in both the target and comparison while its dispersion changes, the scores of frequency-based measures will remain unchanged.</item> <item>For dispersion-based measures (Eta, Zeta, and logarithmic Zeta, Wilcoxon rank-sum test), we hypothesize that they should not show any variation in scores when frequency changes while dispersion remains constant. </item> <item>However, they should be sensitive to variations in dispersion when frequency remains constant, as the number of segments containing the target word is crucial for their calculation.</item> <item>Regarding TF-IDF-based measure, we expect it to exhibit moderate sensitivity in both frequency and dispersion manipulations. This is because TF-IDF is based on term frequency, but the number of segments containing the target word also significantly influences its calculation.</item> </list>