Whenever you run multiple statistical tests on the same set of data, you run into the problem of the Familywise Error Rate. What this means is that the true probability of a type 1 error somewhere in the family of tests you’re running is actually higher than the alpha=.05 you’re using for any given test.
This is a complicated and controversial issue in statistics — even statisticians argue about whether it’s a problem, when it’s a problem, and what to do about it.
In this webinar, we’ll talk about the meaning and consequences of these issues so you can make informed decisions in your data analysis.
We’ll also go through possible solutions, including post-hoc tests and the false discovery rate.
Note: This webinar is an exclusive benefit for members of the Statistically Speaking Membership Program.
About the Instructor
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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- May 2018 Member Webinar : Adjustments for Multiple Testing: When and How to Handle Multiplicity
- July 2017 Member Webinar: The Multi-Faceted World of Residuals
- January 2015 Member Webinar: ANCOVA (Analysis of Covariance)
- January 2014 Member Webinar: Interactions in ANOVA and Regression Models, Part 2