10.5061/DRYAD.8S574
Welz, Anke
Koblenz University of Applied Sciences
Ruxton, Graeme D.
University of St Andrews
Neuhäuser, Markus
Koblenz University of Applied Sciences
Data from: A non-parametric maximum test for the Behrens–Fisher problem
Dryad
dataset
2019
2019-01-24T00:00:00Z
2019-01-24T00:00:00Z
en
https://doi.org/10.1080/00949655.2018.1431236
6267 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Non-normality and heteroscedasticity are common in applications. For the
comparison of two samples in the non-parametric Behrens–Fisher problem,
different tests have been proposed, but no single test can be recommended
for all situations. Here, we propose combining two tests, the Welch t test
based on ranks and the Brunner–Munzel test, within a maximum test.
Simulation studies indicate that this maximum test, performed as a
permutation test, controls the type I error rate and stabilizes the power.
That is, it has good power characteristics for a variety of distributions,
and also for unbalanced sample sizes. Compared to the single tests, the
maximum test shows acceptable type I error control.
MaximumTestExactR code to analyze the example data set with the proposed
maximum test.