Early and Late Buzzards: Comparing Different Approaches for Quantile-based Multiple Testing in Heavy-Tailed Wildlife Research Data

TL;DR

This paper compares various statistical methods for multiple testing in heavy-tailed ecological data, focusing on median and IQR-based approaches, and demonstrates their application in bird breeding research.

Contribution

It introduces and evaluates multiple contrast testing procedures using bootstrap and Bonferroni corrections for median and IQR hypotheses in heavy-tailed data.

Findings

Bootstrap-based methods outperform traditional approaches in heavy-tailed data

Median and IQR are effective alternatives to mean and variance in skewed data

Application to bird breeding data reveals significant ecological trait differences

Abstract

In medical, ecological and psychological research, there is a need for methods to handle multiple testing, for example to consider group comparisons with more than two groups. Typical approaches that deal with multiple testing are mean or variance based which can be less effective in the context of heavy-tailed and skewed data. Here, the median is the preferred measure of location and the interquartile range (IQR) is an adequate alternative to the variance. Therefore, it may be fruitful to formulate research questions of interest in terms of the median or the IQR. For this reason, we compare different inference approaches for two-sided and non-inferiority hypotheses formulated in terms of medians or IQRs in an extensive simulation study. We consider multiple contrast testing procedures combined with a bootstrap method as well as testing procedures with Bonferroni correction. As an example of a multiple testing problem based on heavy-tailed data we analyse an ecological trait variation in early and late breeding in a medium-sized bird of prey.