Hypothesis testing for two population means: parametric or non-parametric test?

Tsagris, Michail; Alenazi, Abdulaziz; Verrou, Kleio-Maria; Pandis, Nikolaos (2019). Hypothesis testing for two population means: parametric or non-parametric test? Journal of statistical computation and simulation, 90(2), pp. 252-270. Taylor & Francis 10.1080/00949655.2019.1677659

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The parametric Welch t-test and the non-parametric Wilcoxon–Mann–Whitney, empirical and exponential empirical likelihood tests are commonly used for hypothesis testing of two population means. In order to circumvent the inflated type I error problem of the nonparametric likelihood testing procedures, a simple calibration using the t distribution and bootstrapping is proposed. Those testing procedures are then being compared via extensive Monte Carlo simulations on the grounds of type I error and power. Evidence is provided supporting that (a) the t calibration and bootstrap improve the type I error of the non-parametric likelihoods, (b) the Welch ttest attains the type I error and produces high levels of power, and (c) the Wilcoxon–Mann–Whitney test produces inflated type I error while computation of the exact p-value is not feasible in the presence of ties. An application to real gene expression data illustrates the computational superiority of the Welch t-test.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > School of Dental Medicine > Department of Orthodontics

UniBE Contributor:

Pandis, Nikolaos

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0094-9655

Publisher:

Taylor & Francis

Language:

English

Submitter:

Renate Imhof-Etter

Date Deposited:

04 Nov 2019 14:11

Last Modified:

08 Dec 2019 02:45

Publisher DOI:

10.1080/00949655.2019.1677659

BORIS DOI:

10.7892/boris.134190

URI:

https://boris.unibe.ch/id/eprint/134190

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