The Significance of Dependency for a Set of k Variables

Martyna Kosińska

doi:10.18778/0208-6018.376.02

Authors

Martyna Kosińska University of Economics in Katowice, Department of Statistics, Econometrics and Mathematics, Katowice, Poland https://orcid.org/0000-0002-5430-227X

DOI:

https://doi.org/10.18778/0208-6018.376.02

Keywords:

permutation tests, multiple comparisons, relationship, statistical significance

Abstract

The issue of conducting multiple statistical tests on the same dataset is widely discussed in the statistical literature. As the number of hypotheses tested increases, so does the probability of committing a type I error. The aim of this article is to present a proposal for a permutation test that allows for testing the significance of the relationship between k variables as a whole. The application of this test yields a single hypothesis test, allowing the type I error to be controlled at the accepted significance level α. The article ends with an empirical analysis based on data from 2014 to 2023. For the years 2014–2017, the existence of statistically significant dependencies were confirmed for a set of three variables (gini coefficient, GDP per capita and unemployment rate).

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