Introduction to non-parametric methods

Course details

statistics seminar | level: beginner
registrations and venue via Ghent University

Abstract

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.

Cost: free!

This seminar is about Introduction to non-parametric methods:
The t-test is robust with respect to assumptions about normality and equivariance and thus is widely applicable. There is another class of methods -nonparametric tests- more suitable for data that come from skewed distributions or have a discrete or ordinal scale. Nonparametric tests such as the sign and Wilcoxon rank-sum tests relax distribution assumptions and are therefore easierto justify, but they come at the cost of lower sensitivity owing to less information inherent in their assumptions. For small samples, the performance of these tests is also constrained because their P values are only coarsely sampled and may have a large minimum. Both issues are mitigated by using larger samples.

Venue:
UGent - Campus Sterre- Building S9
PC room 3.1 Konrad Zuse
Krijgslaan 281, 9000 Gent

Prerequisites

No prerequisites.

Background readings

Instructor

Emmanuel Abatih