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Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty

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  • Andrew A. Anderson

Abstract

Evaluating the importance and the strength of empirical evidence requires asking three questions: First, what are the practical implications of the findings? Second, how precise are the estimates? Confidence intervals provide an intuitive way to communicate precision. Although nontechnical audiences often misinterpret confidence intervals (CIs), I argue that the result is less dangerous than the misunderstandings that arise from hypothesis tests. Third, is the model correctly specified? The validity of point estimates and CIs depends on the soundness of the underlying model.

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  • Andrew A. Anderson, 2019. "Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 118-121, March.
    • Handle: RePEc:taf:amstat:v:73:y:2019:i:s1:p:118-121
    DOI: 10.1080/00031305.2018.1537889
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    Cited by:

    1. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    2. Jae H. Kim, 2022. "Moving to a world beyond p-value," Review of Managerial Science, Springer, vol. 16(8), pages 2467-2493, November.

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