Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance

MLA

Braithwaite Dw, et al. Hedges, Mottes, and Baileys: Causally Ambiguous Statistical Language Can Increase Perceived Study Quality and Policy Relevance. May 2020. EBSCOhost, widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsair&AN=edsair.doi.dedup.....79ea16134a8c33f155116e76434e2645&authtype=sso&custid=ns315887.

APA

Braithwaite Dw, Elizabeth A. Martin, Drew H. Bailey, Daniela Alvarez-Vargas, Moore Mm, Mayan K. Castro, Hugues Lortie-Forgues, & Sirui Wan. (2020). Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance.

Chicago

Braithwaite Dw, Elizabeth A. Martin, Drew H. Bailey, Daniela Alvarez-Vargas, Moore Mm, Mayan K. Castro, Hugues Lortie-Forgues, and Sirui Wan. 2020. “Hedges, Mottes, and Baileys: Causally Ambiguous Statistical Language Can Increase Perceived Study Quality and Policy Relevance,” May. http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsair&AN=edsair.doi.dedup.....79ea16134a8c33f155116e76434e2645&authtype=sso&custid=ns315887.