Proper Interpretation of Results

Lead Author: Peter Bacchetti, PhD

This page provides general conceptual guidance on interpreting results of statistical analyses that are typically used in clinical and translational research, and it links to an interactive tool that provides example text.

Click here to go straight to the interactive tool

A common problem in clincial research is interpretation based only on P-values, most notably the fallacy of interpreting P>0.05 as proving or supporting no effect. Interpretation should also reflect the direction and size of the estimated effect, along with the uncertainty around it as shown by the confidence interval.

Interpretation usually requires assessing whether the estimated effect would be large enough to be important if it turned out to be exactly right. "Important" can mean important for patients, for public health, or for scientific understanding. Similar assessments are also needed for the confidence bounds.

Some good principles to follow when interpreting a study's results include: The interactive page at InterpretationText gives sample phrasing of primary results, such as would appear in the Conclusion section of an abstract, based on user-provided assessments of the importance of the estimate and its confidence bounds.

This topic: CTSpedia > WebHome > ContentInterest > ResultsInterpretation
Topic revision: 12 Feb 2012, PeterBacchetti
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