Goodness and lack of fit tests to pretest normality when comparing means

Pablo Flores () and María de Lourdes Palacios ()

Operations Research and Decisions, 2024, vol. 34, issue 1, 119-129

Abstract: Previous studies show that processes related to traditional pretests to prove the perfect fulfillment of assumptions in comparison means tests lead to severe alterations in the overall Type I error probability and power. These problems seem to be overcome when pretests based on an equivalence approach are used. The paper proposes a lack of fit tests based on equivalence to pretest normality on homoscedastic samples with measurable departures from normality. The Type I error probability and power produced by this equivalence pretest are compared with two traditional goodness of fit pretests and with the direct use of the t-Student and Wilcoxon test of means comparison. Furthermore, since the irrelevance limit for the lack of fit test is an arbitrary value, we propose a non-subjective methodology to find it. Results show that this proposed equivalence test controls the overall Type I Error Probability and produces adequate power; therefore, its use is recommended.

Keywords: normality; lack of fit; goodness of fit; equivalence; assumptions; type I error probability (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no1_6.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wut:journl:v:34:y:2024:i:1:p:119-129:id:6

DOI: 10.37190/ord240106

Access Statistics for this article

More articles in Operations Research and Decisions from Wroclaw University of Science and Technology, Faculty of Management Contact information at EDIRC.
Bibliographic data for series maintained by Adam Kasperski ().