Lies, Damn Lies, and Bad Statistics?

Author	dc.contributor.author	Braghetto, Italo
Author	dc.contributor.author	Figueroa-Giralt, Manuel
Admission date	dc.date.accessioned	2021-04-09T17:29:29Z
Available date	dc.date.available	2021-04-09T17:29:29Z
Publication date	dc.date.issued	2020
Cita de ítem	dc.identifier.citation	Obesity Surgery Oct 2020	es_ES
Identifier	dc.identifier.other	10.1007/s11695-020-04470-y
Identifier	dc.identifier.uri	https://repositorio.uchile.cl/handle/2250/179042
Abstract	dc.description.abstract	A wide spectrum of research such as experimental, randomized trials, cohort or epidemiological studies, technical or control case reports, systematic reviews, and meta-analyses has resulted in a huge amount of publications. These studies and publications may be subject to errors due to poor application of statistical tests, which can lead to misinformation, misinterpretation, and erroneous conclusions, sometimes even considered as lies. In this article, some ideas about this issue are discussed in order to adopt new directions in the future and thus avoid lies and bad statistics.	es_ES
Lenguage	dc.language.iso	en	es_ES
Publisher	dc.publisher	Springer	es_ES
Source	dc.source	Obesity Surgery	es_ES
Keywords	dc.subject	Publications	es_ES
Keywords	dc.subject	Statistic	es_ES
Título	dc.title	Lies, Damn Lies, and Bad Statistics?	es_ES
Document type	dc.type	Artículo de revista
dcterms.accessRights	dcterms.accessRights	Acceso a solo metadatos	es_ES
Cataloguer	uchile.catalogador	crb	es_ES
Indexation	uchile.index	Artículo de publicación ISI	es_ES