Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty.
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...
| Publicado en: | American Statistician Vol. 73; pp. 118 - 122 |
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| Formato: | Artículo |
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Taylor & Francis Ltd
Mar2019 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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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