A Critical Assessment of Null Hypothesis Significance Testing in Quantitative Communication Research

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Fair Statistical Communication in HCI (2016, background): “Methodologists who remain attached to p-values (APA, 2010; Abelson, 1995; Levine et al, 2008b) suggest reporting everything: p-values, point estimates of effect sizes, and their confidence intervals.”
An Agenda for Open Science in Communication (2020, methodology): “In order to determine the generalizability of the results, a common approach to analyzing these empirical data is the use of frequentist null hypothesis significance testing (NHST; Levine, Weber, Hullett, Park, & Lindsey, 2008).”
Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “…of significance tests by scientists (Grant 1962, Henkel 1976, Oakes 1986, Sedlmeier & Gigerenzer 1989, Cohen 1990, Altman 1991, Inman 1994, Schmidt 1996, Hurlbert 1998, Johnson 1999, Marden 2000, Tryon 2001, Hurlbert & Lombardi 2003, Balluerka et al. 2005, Levine et al. 2008a, b).”
A Communication Researchers' Guide to Null Hypothesis Significance Testing and Alternatives. (2008, unknown): “Severe problems with standard NHST exist (Levine et al., 2008).”