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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Bibliographic Details
Published in:The American statistician 2019-03, Vol.73 (sup1), p.118-121
Main Author: Anderson, Andrew A.
Format: Article
Language:English
Subjects:
Confidence intervals
Estimates
Inference
Regression analysis
Robustness
Sampling error
Statistical analysis
Statistical methods
Statistics
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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.
ISSN:0003-1305
1537-2731
DOI:10.1080/00031305.2018.1537889