%0 Journal Article
%T Beyond reporting statistical significance: Identifying informative effect sizes to improve scientific communication
%A David MA Mehler
%A Paul HP Hanel
%J Public Understanding of Science
%@ 1361-6609
%D 2019
%R 10.1177/0963662519834193
%X Transparent communication of research is key to foster understanding within and beyond the scientific community. An increased focus on reporting effect sizes in addition to p value¨Cbased significance statements or Bayes Factors may improve scientific communication with the general public. Across three studies (N£¿=£¿652), we compared subjective informativeness ratings for five effect sizes, Bayes Factor, and commonly used significance statements. Results showed that Cohen¡¯s U3 was rated as most informative. For example, 440 participants (69%) found U3 more informative than Cohen¡¯s d, while 95 (15%) found d more informative than U3, with 99 participants (16%) finding both effect sizes equally informative. This effect was not moderated by level of education. We therefore suggest that in general, Cohen¡¯s U3 is used when scientific findings are communicated. However, the choice of the effect size may vary depending on what a researcher wants to highlight (e.g. differences or similarities)
%K Cohen¡¯s d
%K Cohen¡¯s U3
%K effect size
%K scientific communication
%K statistical communication
%K statistical significance
%U https://journals.sagepub.com/doi/full/10.1177/0963662519834193