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Departments of Preventive Medicine and Biometrics and of Physiology and Biophysics, School of Medicine, University of Colorado Health Sciences Center, Denver, Colorado 80262
Statistical procedures underpin the process of scientific discovery. As
researchers, one way we use these procedures is to test the validity of
a null hypothesis. Often, we test the validity of more than one null
hypothesis. If we fail to use an appropriate procedure to account for
this multiplicity, then we are more likely to reach a wrong scientific
conclusion
we are more likely to make a mistake. In physiology,
experiments that involve multiple comparisons are common: of the
original articles published in 1997 by the American Physiological
Society, ~40% cite a multiple comparison procedure. In this review,
I demonstrate the statistical issue embedded in multiple comparisons,
and I summarize the philosophies of handling this issue. I also
illustrate the three proceduresNewman-Keuls, Bonferroni, least
significant differencecited most often in my literature review; each
of these procedures is of limited practical value. Last, I demonstrate
the false discovery rate procedure, a promising development in multiple
comparisons. The false discovery rate procedure may be the best
practical solution to the problems of multiple comparisons that exist
within physiology and other scientific disciplines.
Bonferroni inequality, false discovery rate, least significant difference, Newman-Keuls, statistics
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