Early and Late Buzzards: Comparing Different Approaches for Quantile-Based Multiple Testing in Heavy-Tailed Wildlife...

Area of Science:

• Statistics
• Ecology
• Biostatistics

Background:

• Multiple testing is crucial in medical, ecological, and psychological research.
• Traditional mean- or variance-based methods struggle with heavy-tailed and skewed data.
• Medians and interquartile ranges (IQRs) are more suitable for such data distributions.

Purpose of the Study:

• To compare statistical inference approaches for hypotheses concerning medians and IQRs.
• To evaluate methods for multiple testing in the presence of heavy-tailed and skewed data.
• To provide robust statistical tools for complex group comparisons.

Main Methods:

• Extensive simulation study comparing different inference approaches.
• Utilized multiple contrast testing procedures with bootstrap methods.
• Included testing procedures with Bonferroni correction for comparison.
• Analyzed ecological trait variation in birds as a real-world example.

Main Results:

• Evaluated the performance of median- and IQR-based inference methods.
• Assessed the effectiveness of bootstrap and Bonferroni correction in multiple testing scenarios.
• Demonstrated the applicability of these methods to ecological data with heavy-tailed distributions.

Conclusions:

• Median- and IQR-based methods offer a robust alternative for multiple testing with skewed data.
• Bootstrap and contrast testing procedures show promise for complex ecological and medical studies.
• The findings support the use of robust statistics in fields with non-normally distributed data.