inferential statistics, substantive significance, causality, confounding, generalizability
Students often believe that statistical significance is the only determinant of whether a quantitative result is “important.” In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction, causality, generalizability, and changeability of the independent variable. I illustrate these issues with examples from an empirical study of the association between how much time teenagers spent playing video games and time spent reading. I describe how study design and context determine each of those aspects of “importance,” and close by summarizing how to provide a holistic view of importance when writing about a quantitative analysis. I also include exercises to guide students through applying these concepts to articles in newspapers and scholarly journals.
Miller, Jane E.. "Beyond Statistical Significance: A Holistic View of What Makes a Research Finding "Important"." Numeracy 16, Iss. 1 (2023): Article 6. DOI: https://doi.org/10.5038/1936-46220.127.116.118
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