Applications of statistical experimental designs to improve statistical inference in weed management.

In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can desig...

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Autores principales:	Steven B Kim, Dong Sub Kim, Christina Magana-Ramirez
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9984312790cc41cd8c6f8b3041ddda43
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spelling	oai:doaj.org-article:9984312790cc41cd8c6f8b3041ddda432021-12-02T20:14:36ZApplications of statistical experimental designs to improve statistical inference in weed management.1932-620310.1371/journal.pone.0257472https://doaj.org/article/9984312790cc41cd8c6f8b3041ddda432021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257472https://doaj.org/toc/1932-6203In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can design an experiment by unevenly distributing experimental units to increase statistical information about the parameter of interest. An additional way of improving an experiment is an adaptive design (e.g., spending the total sample size in multiple steps). It is helpful to have some knowledge about the parameter of interest to design an experiment. In the initial phase of an experiment, a researcher may spend a portion of the total sample size to learn about the parameter of interest. In the later phase, the remaining portion of the sample size can be distributed in order to gain more information about the parameter of interest. Though such ideas have existed in statistical literature, they have not been applied broadly in agricultural studies. In this article, we used simulations to demonstrate the superiority of the experimental designs over the balanced designs under three practical situations: comparing two groups, studying a dose-response relationship with right-censored data, and studying a synergetic effect of two treatments. The simulations showed that an objective-specific design provides smaller error in parameter estimation and higher statistical power in hypothesis testing when compared to a balanced design. We also conducted an adaptive experimental design applied to a dose-response study with right-censored data to quantify the effect of ethanol on weed control. Retrospective simulations supported the benefit of this adaptive design as well. All researchers face different practical situations, and appropriate experimental designs will help utilize available resources efficiently.Steven B KimDong Sub KimChristina Magana-RamirezPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257472 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Steven B Kim Dong Sub Kim Christina Magana-Ramirez Applications of statistical experimental designs to improve statistical inference in weed management.
description	In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can design an experiment by unevenly distributing experimental units to increase statistical information about the parameter of interest. An additional way of improving an experiment is an adaptive design (e.g., spending the total sample size in multiple steps). It is helpful to have some knowledge about the parameter of interest to design an experiment. In the initial phase of an experiment, a researcher may spend a portion of the total sample size to learn about the parameter of interest. In the later phase, the remaining portion of the sample size can be distributed in order to gain more information about the parameter of interest. Though such ideas have existed in statistical literature, they have not been applied broadly in agricultural studies. In this article, we used simulations to demonstrate the superiority of the experimental designs over the balanced designs under three practical situations: comparing two groups, studying a dose-response relationship with right-censored data, and studying a synergetic effect of two treatments. The simulations showed that an objective-specific design provides smaller error in parameter estimation and higher statistical power in hypothesis testing when compared to a balanced design. We also conducted an adaptive experimental design applied to a dose-response study with right-censored data to quantify the effect of ethanol on weed control. Retrospective simulations supported the benefit of this adaptive design as well. All researchers face different practical situations, and appropriate experimental designs will help utilize available resources efficiently.
format	article
author	Steven B Kim Dong Sub Kim Christina Magana-Ramirez
author_facet	Steven B Kim Dong Sub Kim Christina Magana-Ramirez
author_sort	Steven B Kim
title	Applications of statistical experimental designs to improve statistical inference in weed management.
title_short	Applications of statistical experimental designs to improve statistical inference in weed management.
title_full	Applications of statistical experimental designs to improve statistical inference in weed management.
title_fullStr	Applications of statistical experimental designs to improve statistical inference in weed management.
title_full_unstemmed	Applications of statistical experimental designs to improve statistical inference in weed management.
title_sort	applications of statistical experimental designs to improve statistical inference in weed management.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9984312790cc41cd8c6f8b3041ddda43
work_keys_str_mv	AT stevenbkim applicationsofstatisticalexperimentaldesignstoimprovestatisticalinferenceinweedmanagement AT dongsubkim applicationsofstatisticalexperimentaldesignstoimprovestatisticalinferenceinweedmanagement AT christinamaganaramirez applicationsofstatisticalexperimentaldesignstoimprovestatisticalinferenceinweedmanagement
_version_	1718374661373296640

Applications of statistical experimental designs to improve statistical inference in weed management.

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