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Confronting low data reduction typologies, as established by using data from parallel texts, with the high data reduction typologies of WALS reveals a systematic bias of WALS typologies toward highly bimodal distribution. Properties with a distribution supporting a discrete feature analysis in many languages are likelier to be represented in WALS and to be represented accurately. This bias has important consequences when WALS typologies are interpreted theoretically or further processed statistically.
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