Tests of significance and effect size: Meaningful interpretation of statistical data in the health sciences

Learning to interpret and apply statistical principles is necessary for advanced study in the health professions world-wide. Because data play a critical role in a wide variety of biomedical and health-related studies, it is important to bring statistics to the forefront and discuss the implications of underlying data that often seem ambiguous to us. Case studies involving health-related statistics are often seen in a less than favorable light as the popular media, as well as researchers, misinterpret the data, collect certain data in large numbers yet miss certain critical measures, and do not always have replicable results to deliver. The focus of this paper is to discuss the concept of statistical significance with respect to meaningful effect size, citing examples of how experts reach different conclusions to national and global health science problems that are being analyzed. These insights should be helpful to researchers as they formulate and test hypotheses and draw appropriate conclusions