“An Overview of Multiple Comparison Methods”
Elizabeth Schifano, PhD
UConn, Department of Statistics
Tuesday, October 21, 2014
3:00-4:15pm
Description
In this workshop, Dr. Schifano will demonstrate when the problem of multiple comparisons (or testing) occurs, and how inference regarding Type-I error is affected. Traditional methods for correcting for multiple testing will be discussed. These traditional methods are generally not suitable for large-scale experiments, so Dr. Schifano will additionally describe some of the more modern methods for correcting for multiple testing. In particular, she will explain the notions of family-wise error rates (FWER) and false discovery rates (FDR). She also will show how different software packages account for multiple testing. This workshop requires basic knowledge in hypothesis testing (t-test, ANOVA).
Location
The lecture will take place in CHIP Colloquium Room (14) on the first floor of the Ryan Building (in the lounge area) at 2006 Hillside Road at the University of Connecticut in Storrs. For directions and maps, see http://www.chip.uconn.edu/about/directions-to-chip/.
Web Stream
There will be no web stream for this talk.
About the Speaker
Elizabeth Schifano earned her PhD in Statistics at Cornell University in 2010, and then completed her postdoctoral training in Biostatistics at the Harvard School of Public Health in 2012. This is currently her third year as an Assistant Professor in Statistics at the University of Connecticut.
RSVP
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More information available at: http://www.chip.uconn.edu/lecture-series/fall-2014-schedule/
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