25 Mar Multiple Testing Procedures (Video lecture via Bongo Virtual Classroom)
1:00 pm - 2:30 pm
statistics seminar | level: beginner
registrations and venue via Ghent University
Are you concerned about how reproducible your data derived results will be? Have you seen statistics in a class but want to dig deeper? Are you using a statistical method but wonder if it is the best one to use?
The editors of Nature appreciate that it is tricky enough for a scientist to keep up-to-date in their own field, let alone in the ever expanding field of statistics. To help ease the burden on scientists, they have introduced a column on statistics to one of their publications, Nature Methods, called Points of Significance.
On regular occasions during the academic year, a statistician from FIRE or FLAMES will lead a discussion of a statistics topic from a Points of Significance article. We will start with the basics, that is, with the idea of sampling, work our way through a detailed discussion of ANOVA and end with the topic of Bayesian statistics.
This seminar is about Multiple Testing Procedures:
Multiple testing refers to any instance that involves the simultaneous testing of more than one hypothesis. If decisions about the individual hypotheses are based on the unadjusted marginal p-values, then there is typically a large probability (typically 5% if all null hypothesis are true) that some of the true null hypotheses will be rejected. Unfortunately, such a course of action is still common. In this seminar, the problem of multiple testing will be formally introduced and methods used for their correction will be discussed. More specifically, we introduce Family wise error rate (FWER) adjustments for medium scale data mining and False discovery rate ( FDR) for mega-scale data mining. Multiplicity adjustments for non-parametric tests will also be considered.
Video lecture via Bongo Virtual Classroom