I've had friends in biochem "wet" labs who've asked me to do some simple statistics on some of their results. This looks like an interesting seminar to attend if you've ever thought about doing a t-test on fold changes in some outcome measure between treatment and control groups, a pretty common outcome in biochemical assays. If the speaker provides slides electronically I'll happily post them here after the seminar.
Department of Biostatistics Seminar/Workshop Series:
T-Test on Fold Changes
Tatsuki Koyama, PhD
Assistant Professor of Biostatistics, Cancer Biostatistics Center, Vanderbilt-Ingram Cancer Center
Wednesday, June 16, 1:30-2:30pm, MRBIII Conference Room 1220
Basic science experiments often use a separate control group for each treatment group. Typically, the treatment group outcomes are scaled by the average of the corresponding control group outcomes. Despite its overwhelming popularity, this "fold change" method has serious statistical problems resulting in reduced validity. When the implicit variability of the control group outcomes is ignored, a large type I error inflation can result. Likewise, this scaling induces correlation and can substantially inflate the type I error when this correlation is ignored. We present simulations showing that this inflation results in type I error rates as high as 50% in everyday settings. We propose some computational and analytical approaches for dealing with this problem, and we present some practical recommendations for experimental designs with small sample sizes. Intended audience: Clinical and basic science researchers and statisticians.