Rigorous Statistical Methods for Rigorous Microbiome Science

ABSTRACT High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodivers...

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Main Author:	Amy D. Willis
Format:	article
Language:	EN
Published:	American Society for Microbiology 2019
Subjects:	hypothesis testing machine learning modeling reproducibility statistics Microbiology QR1-502
Online Access:	https://doaj.org/article/8a685d4d7a404b6d888906e960bb251d
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spelling	oai:doaj.org-article:8a685d4d7a404b6d888906e960bb251d2021-12-02T19:46:18ZRigorous Statistical Methods for Rigorous Microbiome Science10.1128/mSystems.00117-192379-5077https://doaj.org/article/8a685d4d7a404b6d888906e960bb251d2019-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00117-19https://doaj.org/toc/2379-5077ABSTRACT High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodiversity data. Developing statistical methods that produce valid P values requires thoughtful modeling and careful validation, but careful statistical analysis reduces the risk of false discoveries and increases scientific understanding.Amy D. WillisAmerican Society for Microbiologyarticlehypothesis testingmachine learningmodelingreproducibilitystatisticsMicrobiologyQR1-502ENmSystems, Vol 4, Iss 3 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	hypothesis testing machine learning modeling reproducibility statistics Microbiology QR1-502
spellingShingle	hypothesis testing machine learning modeling reproducibility statistics Microbiology QR1-502 Amy D. Willis Rigorous Statistical Methods for Rigorous Microbiome Science
description	ABSTRACT High-throughput sequencing has facilitated discovery in microbiome science, but distinguishing true discoveries from spurious signals can be challenging. The Statistical Diversity Lab develops rigorous statistical methods and statistical software for the analysis of microbiome and biodiversity data. Developing statistical methods that produce valid P values requires thoughtful modeling and careful validation, but careful statistical analysis reduces the risk of false discoveries and increases scientific understanding.
format	article
author	Amy D. Willis
author_facet	Amy D. Willis
author_sort	Amy D. Willis
title	Rigorous Statistical Methods for Rigorous Microbiome Science
title_short	Rigorous Statistical Methods for Rigorous Microbiome Science
title_full	Rigorous Statistical Methods for Rigorous Microbiome Science
title_fullStr	Rigorous Statistical Methods for Rigorous Microbiome Science
title_full_unstemmed	Rigorous Statistical Methods for Rigorous Microbiome Science
title_sort	rigorous statistical methods for rigorous microbiome science
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/8a685d4d7a404b6d888906e960bb251d
work_keys_str_mv	AT amydwillis rigorousstatisticalmethodsforrigorousmicrobiomescience
_version_	1718376037387075584

Rigorous Statistical Methods for Rigorous Microbiome Science

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