The problem with unadjusted multiple and sequential statistical testing

In research studies, the need for additional samples to obtain sufficient statistical power has often to be balanced with the experimental costs. One approach to this end is to sequentially collect data until you have sufficient measurements, e.g., when the p-value drops below 0.05. I outline that t...

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Autor principal:	Casper Albers
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a9436a416b4043f181cc7fde477873ff
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spelling	oai:doaj.org-article:a9436a416b4043f181cc7fde477873ff2021-12-02T15:35:05ZThe problem with unadjusted multiple and sequential statistical testing10.1038/s41467-019-09941-02041-1723https://doaj.org/article/a9436a416b4043f181cc7fde477873ff2019-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-09941-0https://doaj.org/toc/2041-1723In research studies, the need for additional samples to obtain sufficient statistical power has often to be balanced with the experimental costs. One approach to this end is to sequentially collect data until you have sufficient measurements, e.g., when the p-value drops below 0.05. I outline that this approach is common, yet that unadjusted sequential sampling leads to severe statistical issues, such as an inflated rate of false positive findings. As a consequence, the results of such studies are untrustworthy. I identify the statistical methods that can be implemented in order to account for sequential sampling.Casper AlbersNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-4 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Casper Albers The problem with unadjusted multiple and sequential statistical testing
description	In research studies, the need for additional samples to obtain sufficient statistical power has often to be balanced with the experimental costs. One approach to this end is to sequentially collect data until you have sufficient measurements, e.g., when the p-value drops below 0.05. I outline that this approach is common, yet that unadjusted sequential sampling leads to severe statistical issues, such as an inflated rate of false positive findings. As a consequence, the results of such studies are untrustworthy. I identify the statistical methods that can be implemented in order to account for sequential sampling.
format	article
author	Casper Albers
author_facet	Casper Albers
author_sort	Casper Albers
title	The problem with unadjusted multiple and sequential statistical testing
title_short	The problem with unadjusted multiple and sequential statistical testing
title_full	The problem with unadjusted multiple and sequential statistical testing
title_fullStr	The problem with unadjusted multiple and sequential statistical testing
title_full_unstemmed	The problem with unadjusted multiple and sequential statistical testing
title_sort	problem with unadjusted multiple and sequential statistical testing
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/a9436a416b4043f181cc7fde477873ff
work_keys_str_mv	AT casperalbers theproblemwithunadjustedmultipleandsequentialstatisticaltesting AT casperalbers problemwithunadjustedmultipleandsequentialstatisticaltesting
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The problem with unadjusted multiple and sequential statistical testing

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