most citedThe Kruskal Wallis test can not be recommended

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

stat.ME2024

Unlocking Insights: Enhanced Analysis of Covariance in General Factorial Designs through Multiple Contrast Tests under Variance Heteroscedasticity

Matthias Becher, Ludwig A. Hothorn, Frank Konietschke

A common goal in clinical trials is to conduct tests on estimated treatment effects adjusted for covariates such as age or sex. Analysis of Covariance (ANCOVA) is often used in the…

stat.ME2024

Bartholomew's trend test -- approximated by a multiple contrast test

Ludwig A. Hothorn

Bartholomew's trend test belongs to the broad class of isotonic regression models, specifically with a single qualitative factor, e.g. dose levels. Using the approximation of the A…

stat.AP2023

Tests for strict monotonic trend in bio-medical dose-response relationships (respective concentration-response or exposure-response relationships) -- a biostatistical perspective

Ludwig A. Hothorn

Evidence of a global trend in dose-response dependencies is commonly used in bio-medicine and epidemiology, especially because this represents a causality criterion. However, conve…

stat.ME2023

Consistent ANOVA-type tests for various effect sizes

Ludwig A. Hothorn

Analysis of variance (ANOVA) reveals some disadvantages, such as non-robustness against heteroscedastic or non-normal errors and using difference to overall mean as effect sizes on…

stat.ME2023

The Dunnett procedure with possibly heterogeneous variances

Ludwig A. Hothorn, Mario Hasler

Most comparisons of treatments or doses against a control are performed by the original Dunnett single step procedure \cite{Dunnett1955} providing both adjusted p-values and simult…

stat.ME20231 cited

The Kruskal Wallis test can not be recommended

Ludwig A. Hothorn

Although the Kruskal-Wallis (KW) test is widely used, it should not be recommended: it is not robust to arbitrary alternatives, it is only a global test without confidence interval…