How should we evaluate the results from a quantitative study?  A common, but unfortunate way is to examine the p-value:  If it's not significant, the study wasn't good; if it's significant, then good, and if it's highly significant, then very good.  This talk introduces Robert Abelson's MAGIC criteria for evaluating quantitative results. 

 

The NC TraCS Biostatistics Seminar Series helps clinical and translational researchers collaborate more effectively with consulting biostatisticians by building deeper understanding of key statistical concepts and methods. Researchers then are better able to (1) evaluate relevance of the concept or method for research aim(s) definition and choice of study design; and (2) properly interpret the results of data analysis.

How should we evaluate the results from a quantitative study? A common, but unfortunate way is to examine the p-value. If it's not significant, the study wasn't good; if it's significant, then good; and if it's highly significant, then very good. In this seminar, Jeff Laux, PhD, will introduce Robert Abelson's MAGIC criteria for evaluating quantitative results.

Laux is a consulting biostatistician and research associate with the Biostatistics Service at NC TraCS Institute and an adjunct instructor with the Department of Biostatistics in the Gillings School of Global Public Health.


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