Abstract
One challenge when communicating science to practitioners and the general public is accurately representing statistical results. In particular, describing the meaning of statistical significance to a non-scientific audience is especially difficult given the technical nature of a correct definition. Correct interpretations of statistical significance can be unintuitive, nuanced, and use unfamiliar technical language. As a result, when researchers are tasked with providing short and understandable interpretations of statistical significance it can be tempting to default to convenient but incorrect interpretations. In the current paper, we offer a concise, simple, and correct interpretation of statistical significance that is suitable for communications targeting a general audience.
Highlights
For researchers in applied fields like industrial/organizational (I/O) psychology that follow the scientist-practitioner model, it is important to be able to disseminate knowledge and communicate science to non-scientific audiences
Beginning with the assumption that the true effect is zero, a p-value indicates the proportion of test statistics, computed from hypothetical random samples, that are as extreme, or more extreme, than the test statistic observed in the current study
Researchers in applied fields like I/O psychology are often required to communicate and interpret what statistical significance means to non-scientific audiences
Summary
For researchers in applied fields like industrial/organizational (I/O) psychology that follow the scientist-practitioner model, it is important to be able to disseminate knowledge and communicate science to non-scientific audiences. Rejecting it means that you leave it up to readers to know or figure out for themselves what statistical significance means Modifying it means that you have the difficult task of providing an easy-to-read, but correct, definition of statistical significance for a general audience. Spotting counterfeit definitions can be so difficult that even those with formal training on the subject of statistical significance can have difficulty distinguishing correct from incorrect definitions and often make interpretational errors (e.g., Haller and Krauss, 2002; Lecoutre et al, 2003; Hoekstra et al, 2006; Castro Sotos et al, 2009) One implication of these issues is that if a researcher is tasked with providing an understandable definition of statistical significance it can be easy to default to inaccurate definitions and commonly used fallacies. A review of the history of NHST criticisms reveals that researchers’ misunderstanding, misinterpretation, and misapplication of the technique is common but is a contributing factor leading to other criticisms (e.g., Bakan, 1966; Carver, 1978; Cohen, 1994)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
