Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations

Water quality monitoring programs commonly use the Mann-Kendall test or linear regression to identify statistically significant monotonic trends in fecal indicator bacteria concentrations (typically Escherichia coli [E. coli]). The statistical power of these tests to detect trends of different magn...

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Autor principal:	Michael Paul Schramm
Formato:	article
Lenguaje:	EN
Publicado:	Texas Water Journal 2021
Materias:	trend detection E. coli statistical power water quality Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/0f8af244f9574a0b8a2c127ab3065054
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spelling	oai:doaj.org-article:0f8af244f9574a0b8a2c127ab30650542021-11-19T19:21:29ZEstimating statistical power for detecting long term trends in surface water Escherichia coli concentrations10.21423/twj.v12i1.71262160-5319https://doaj.org/article/0f8af244f9574a0b8a2c127ab30650542021-10-01T00:00:00Zhttps://journals.tdl.org/twj/index.php/twj/article/view/7126https://doaj.org/toc/2160-5319 Water quality monitoring programs commonly use the Mann-Kendall test or linear regression to identify statistically significant monotonic trends in fecal indicator bacteria concentrations (typically Escherichia coli [E. coli]). The statistical power of these tests to detect trends of different magnitudes (effect size) is rarely communicated to stakeholders, and it is unlikely they are considered when designing monitoring schedules. The statistical power for detecting trends in surface water E. coli bacteria concentrations using Mann-Kendall and linear regression at water quality monitoring sites across Texas was estimated using Monte Carlo simulation. The probability that an individual water quality monitoring site in Texas had adequate statistical power was also estimated using logistic regression. Mann-Kendall and linear regression trend tests show similar statistical power. Both tests are unlikely to achieve adequate statistical power when E. coli concentrations decrease by 20% or less over 7 years under most sampling frequencies. To adequately detect concentration decreases of 30% to 40% over 7 years, monthly sampling is required. Because many sites across Texas are sampled quarterly, monotonic trends tests will not be powerful enough to detect trends of moderate magnitudes. To better facilitate stakeholder decision-making, it is important to communicate the relative power of statistical tests and detectible magnitudes of changes. I suggest data analysts conduct power analyses to improve monitoring program designs and improve communication of trend test limitations. Software and training for water quality analysts could facilitate communication of power and effect sizes. Alternative trend assessment methods may be more reliable for describing changes in fecal indicator bacteria concentrations. Michael Paul SchrammTexas Water Journalarticletrend detectionE. colistatistical powerwater qualityWater supply for domestic and industrial purposesTD201-500ENTexas Water Journal, Vol 12, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	trend detection E. coli statistical power water quality Water supply for domestic and industrial purposes TD201-500
spellingShingle	trend detection E. coli statistical power water quality Water supply for domestic and industrial purposes TD201-500 Michael Paul Schramm Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations
description	Water quality monitoring programs commonly use the Mann-Kendall test or linear regression to identify statistically significant monotonic trends in fecal indicator bacteria concentrations (typically Escherichia coli [E. coli]). The statistical power of these tests to detect trends of different magnitudes (effect size) is rarely communicated to stakeholders, and it is unlikely they are considered when designing monitoring schedules. The statistical power for detecting trends in surface water E. coli bacteria concentrations using Mann-Kendall and linear regression at water quality monitoring sites across Texas was estimated using Monte Carlo simulation. The probability that an individual water quality monitoring site in Texas had adequate statistical power was also estimated using logistic regression. Mann-Kendall and linear regression trend tests show similar statistical power. Both tests are unlikely to achieve adequate statistical power when E. coli concentrations decrease by 20% or less over 7 years under most sampling frequencies. To adequately detect concentration decreases of 30% to 40% over 7 years, monthly sampling is required. Because many sites across Texas are sampled quarterly, monotonic trends tests will not be powerful enough to detect trends of moderate magnitudes. To better facilitate stakeholder decision-making, it is important to communicate the relative power of statistical tests and detectible magnitudes of changes. I suggest data analysts conduct power analyses to improve monitoring program designs and improve communication of trend test limitations. Software and training for water quality analysts could facilitate communication of power and effect sizes. Alternative trend assessment methods may be more reliable for describing changes in fecal indicator bacteria concentrations.
format	article
author	Michael Paul Schramm
author_facet	Michael Paul Schramm
author_sort	Michael Paul Schramm
title	Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations
title_short	Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations
title_full	Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations
title_fullStr	Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations
title_full_unstemmed	Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations
title_sort	estimating statistical power for detecting long term trends in surface water escherichia coli concentrations
publisher	Texas Water Journal
publishDate	2021
url	https://doaj.org/article/0f8af244f9574a0b8a2c127ab3065054
work_keys_str_mv	AT michaelpaulschramm estimatingstatisticalpowerfordetectinglongtermtrendsinsurfacewaterescherichiacoliconcentrations
_version_	1718419938003124224

Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations

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