@article{4666eb20-1142-4a4f-8287-a5888974fc57,
author = {Jacek Pietraszek, Andrzej Sobczyk, Ewa Skrzypczak-Pietraszek, Maciej Kołomycki},
title = {The fuzzy interpretation of the statistical test for irregular data},
journal = {Technical Transactions},
volume = {2016},
number = {Mechanics Issue 4-M 2016},
year = {2016},
issn = {0011-4561},
pages = {119-125},keywords = {statistical test; normality of distribution; fuzzy statistics; bootstrap},
abstract = {The well-known statistical tests have been developed on the basis of many additional assumptions, among which the normality of a data source distribution is one of the most important. The outcome of a test is a p-value which may is interpreted as an estimation of a risk for a false negative decision i.e. it is an answer to the question “how much do I risk if I deny?”. This risk estimation is a base for a decision (after comparing with a significance level α): reject or not. This sharp trigger – p-level greater than α or not – ignores the fact that a context is rather smooth and evolves from “may be” through “rather not” to “certainly not”. An alternative option for such assessments is proposed by a fuzzy statistics, particularly by Buckley’s approach. The fuzzy approach introduces a better scale for expressing decision uncertainty. This paper compares three approaches: a classic one based on a normality assumption, Buckley’s theoretical one and a bootstrap-based one},
doi = {10.4467/2353737XCT.16.242.5991},
url = {https://ejournals.eu/en/journal/czasopismo-techniczne/article/the-fuzzy-interpretation-of-the-statistical-test-for-irregular-data}
}