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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...

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Published in:American Statistician Vol. 73; pp. 118 - 122
Main Author: Anderson, Andrew A.
Format: Article
Published: Taylor & Francis Ltd Mar2019 Supplement
Subjects:
Quantitative research
Statistical models
Statistical reliability
Measurement uncertainty (Statistics)
Sampling errors
Online Access:View this record in EBSCOhost
Summary: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.