Member Training: Adjustments for Multiple Testing: When and How to Handle Multiplicity

After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times for each test.  When you figure out the probability of Type I error across all the tests, that probability skyrockets.

There are a number of ways to control for this increase in chance significance. And as with most things statistical, determining a viable adjustment to control for the chance significance depends on what you are doing. Some approaches are good. Some are not so good. And, sometimes an adjustment isn’t even necessary.

In this webinar, Elaine Eisenbeisz will provide an overview of multiple comparisons and why they can be a problem.

She will explain the differences between family wise error rate (FWER) and false discovery rate (FDR). She also will present many options for adjusting statistical tests and explain why pre-planning the corrections, if any, for your study is paramount to a robust research study.

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Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions. She designs methodology and analyzes data for studies in the clinical, and biotechnology fields. Additionally, Elaine and Omega Statistics are the go-to resource for ABD students who require assistance with dissertation methodology and analysis.

Throughout her tenure as a private practice statistician, Elaine has published work with researchers and colleagues in peer-reviewed journals. Fitting of her eclectic tastes, her current interests include statistical genetics and psychometric survey development.

Elaine earned her B.S. in Statistics at UC Riverside and her Master’s Certification in Applied Statistics from Texas A&M. She is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society.

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