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

ABSTRACT: There is a norm in psychology to use causally ambiguous statistical language, rather than straightforward causal language, when describing methods and results of nonexperimental studies. However, causally ambiguous language may inhibit a critical examination of the study's causal assumptions and lead to a greater acceptance of policy recommendations that rely on causal interpretations of nonexperimental findings. In a preregistered experiment, 142 psychology faculty, postdocs, and doctoral students (54% female), ages 22-67 (M = 33.20, SD = 8.96), rated the design and analysis from hypothetical studies with causally ambiguous statistical language as of higher quality (by .34-.80 SD) and as similarly or more supportive (by .16-.27 SD) of policy recommendations than studies described in strai

SUBMITTER: Alvarez-Vargas D 

PROVIDER: S-EPMC10602341 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature