Multiple Testing – ONLINE

Course details

statistics seminar | level: intermediate | register now
for questions related to this event, contact ugent@flames-statistics.com
affiliation: Ghent University

Abstract

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 megascale data mining. Multiplicity adjustments for non-parametric tests will also be considered.

Prerequisites

Background readings

Fee

FREE

Venue

Ghent University-Online

Instructor

dr Emmanuel Abatih