A note on tests for relevant differences with extremely large sample sizes.
large sample size
testing for relevant differences
two-tailed hypothesis tests
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
04
07
2018
revised:
29
10
2018
accepted:
30
10
2018
pubmed:
13
11
2018
medline:
31
7
2019
entrez:
13
11
2018
Statut:
ppublish
Résumé
A well-known problem in classical two-tailed hypothesis testing is that P-values go to zero when the sample size goes to infinity, irrespectively of the effect size. This pitfall can make the testing of data consisting of large sample sizes potentially unreliable. In this note, we propose to test for relevant differences to overcome this issue. We illustrate the proposed test a on real data set of about 40 million privately insured patients.
Identifiants
pubmed: 30417414
doi: 10.1002/bimj.201800195
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
162-165Informations de copyright
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.