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This paper argues for using authentic data not only as an empirical basis for linguistic generalizations but also for exemplification purposes in monolingual and particularly in bi- and multilingual contrastive studies. It shows that parallel data extracted from the available parallel corpora can - after enrichment with semantic-functional information while maintaining the available contextual, register-related and linguistic information - serve as a perfect data source for multilingual exemplification. Moreover, the analysis of semantic-functionally equivalent parallel sequences allows the investigation and exemplification of similarities and differences in how different languages express similar meaning from both a semasiological and an onomasiological perspective.
In the first volume of Corpus Linguistics and Linguistic Theory, Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/ cllt.2005.1.2.277. http://www.degruyter.com/view/j/cllt.2005.1.issue-2/cllt.2005. 1.2.277/cllt.2005.1.2.277.xml: 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.