Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison

R. A. Weitzman

doi:doi:10.11648/j.ajtas.20221103.11

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Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison

R. A. Weitzman

Published in American Journal of Theoretical and Applied Statistics (Volume 11, Issue 3)

Received: 17 April 2022 Accepted: 5 May 2022 Published: 12 May 2022

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Abstract

A November 2021 article in the journal Chance on a misuse of statistics by hydrogeologists in their modeling of water levels below ground raises the question of whether climatologists might be committing the same statistical errors in their modeling of global warming above ground. In seeking to answer that question, the research reported in this article finds the answer to be, yes, both research communities corrupt data by altering values of independent variables to reduce error variation or to achieve particular model results. That data alteration not only creates an impermissible negative correlation between estimates and errors but also creates model estimates that exaggerate trends in the observations. The exaggerated trends occur regardless of the nature or the intent of the data alteration. For that reason, use of trends in model estimates resulting from data alteration as a guide to future research or as a basis for conclusions may lead researchers astray. This article suggests an alternative research strategy consisting of random sampling of observation zones which, by limiting a study to thousands rather than millions of zones, could enable researchers to obtain sufficiently accurate input data to make the alteration of data unnecessary. Use of this procedure could also help avoid exaggerated and misleading predictions from models.

Published in	American Journal of Theoretical and Applied Statistics (Volume 11, Issue 3)
DOI	10.11648/j.ajtas.20221103.11
Page(s)	83-88
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Tuning, Calibration, Linear Model, Estimation, Prediction, Error, Global Warming

References

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APA Style

R. A. Weitzman. (2022). Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison. American Journal of Theoretical and Applied Statistics, 11(3), 83-88. https://doi.org/10.11648/j.ajtas.20221103.11

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ACS Style

R. A. Weitzman. Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison. Am. J. Theor. Appl. Stat. 2022, 11(3), 83-88. doi: 10.11648/j.ajtas.20221103.11

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AMA Style

R. A. Weitzman. Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison. Am J Theor Appl Stat. 2022;11(3):83-88. doi: 10.11648/j.ajtas.20221103.11

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@article{10.11648/j.ajtas.20221103.11,
  author = {R. A. Weitzman},
  title = {Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {11},
  number = {3},
  pages = {83-88},
  doi = {10.11648/j.ajtas.20221103.11},
  url = {https://doi.org/10.11648/j.ajtas.20221103.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20221103.11},
  abstract = {A November 2021 article in the journal Chance on a misuse of statistics by hydrogeologists in their modeling of water levels below ground raises the question of whether climatologists might be committing the same statistical errors in their modeling of global warming above ground. In seeking to answer that question, the research reported in this article finds the answer to be, yes, both research communities corrupt data by altering values of independent variables to reduce error variation or to achieve particular model results. That data alteration not only creates an impermissible negative correlation between estimates and errors but also creates model estimates that exaggerate trends in the observations. The exaggerated trends occur regardless of the nature or the intent of the data alteration. For that reason, use of trends in model estimates resulting from data alteration as a guide to future research or as a basis for conclusions may lead researchers astray. This article suggests an alternative research strategy consisting of random sampling of observation zones which, by limiting a study to thousands rather than millions of zones, could enable researchers to obtain sufficiently accurate input data to make the alteration of data unnecessary. Use of this procedure could also help avoid exaggerated and misleading predictions from models.},
 year = {2022}
}

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TY  - JOUR
T1  - Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison
AU  - R. A. Weitzman
Y1  - 2022/05/12
PY  - 2022
N1  - https://doi.org/10.11648/j.ajtas.20221103.11
DO  - 10.11648/j.ajtas.20221103.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 83
EP  - 88
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20221103.11
AB  - A November 2021 article in the journal Chance on a misuse of statistics by hydrogeologists in their modeling of water levels below ground raises the question of whether climatologists might be committing the same statistical errors in their modeling of global warming above ground. In seeking to answer that question, the research reported in this article finds the answer to be, yes, both research communities corrupt data by altering values of independent variables to reduce error variation or to achieve particular model results. That data alteration not only creates an impermissible negative correlation between estimates and errors but also creates model estimates that exaggerate trends in the observations. The exaggerated trends occur regardless of the nature or the intent of the data alteration. For that reason, use of trends in model estimates resulting from data alteration as a guide to future research or as a basis for conclusions may lead researchers astray. This article suggests an alternative research strategy consisting of random sampling of observation zones which, by limiting a study to thousands rather than millions of zones, could enable researchers to obtain sufficiently accurate input data to make the alteration of data unnecessary. Use of this procedure could also help avoid exaggerated and misleading predictions from models.
VL  - 11
IS  - 3
ER  -

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Author Information

R. A. Weitzman

Naval Postgraduate School, Monterey, USA

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Plain Text BibTeX RIS

APA Style

R. A. Weitzman. (2022). Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison. American Journal of Theoretical and Applied Statistics, 11(3), 83-88. https://doi.org/10.11648/j.ajtas.20221103.11

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ACS Style

R. A. Weitzman. Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison. Am. J. Theor. Appl. Stat. 2022, 11(3), 83-88. doi: 10.11648/j.ajtas.20221103.11

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AMA Style

R. A. Weitzman. Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison. Am J Theor Appl Stat. 2022;11(3):83-88. doi: 10.11648/j.ajtas.20221103.11

Copy | Download

@article{10.11648/j.ajtas.20221103.11,
  author = {R. A. Weitzman},
  title = {Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {11},
  number = {3},
  pages = {83-88},
  doi = {10.11648/j.ajtas.20221103.11},
  url = {https://doi.org/10.11648/j.ajtas.20221103.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20221103.11},
  abstract = {A November 2021 article in the journal Chance on a misuse of statistics by hydrogeologists in their modeling of water levels below ground raises the question of whether climatologists might be committing the same statistical errors in their modeling of global warming above ground. In seeking to answer that question, the research reported in this article finds the answer to be, yes, both research communities corrupt data by altering values of independent variables to reduce error variation or to achieve particular model results. That data alteration not only creates an impermissible negative correlation between estimates and errors but also creates model estimates that exaggerate trends in the observations. The exaggerated trends occur regardless of the nature or the intent of the data alteration. For that reason, use of trends in model estimates resulting from data alteration as a guide to future research or as a basis for conclusions may lead researchers astray. This article suggests an alternative research strategy consisting of random sampling of observation zones which, by limiting a study to thousands rather than millions of zones, could enable researchers to obtain sufficiently accurate input data to make the alteration of data unnecessary. Use of this procedure could also help avoid exaggerated and misleading predictions from models.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Tuning a Model in Climatology and Calibrating One in Hydrogeology: An Informative Comparison
AU  - R. A. Weitzman
Y1  - 2022/05/12
PY  - 2022
N1  - https://doi.org/10.11648/j.ajtas.20221103.11
DO  - 10.11648/j.ajtas.20221103.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 83
EP  - 88
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20221103.11
AB  - A November 2021 article in the journal Chance on a misuse of statistics by hydrogeologists in their modeling of water levels below ground raises the question of whether climatologists might be committing the same statistical errors in their modeling of global warming above ground. In seeking to answer that question, the research reported in this article finds the answer to be, yes, both research communities corrupt data by altering values of independent variables to reduce error variation or to achieve particular model results. That data alteration not only creates an impermissible negative correlation between estimates and errors but also creates model estimates that exaggerate trends in the observations. The exaggerated trends occur regardless of the nature or the intent of the data alteration. For that reason, use of trends in model estimates resulting from data alteration as a guide to future research or as a basis for conclusions may lead researchers astray. This article suggests an alternative research strategy consisting of random sampling of observation zones which, by limiting a study to thousands rather than millions of zones, could enable researchers to obtain sufficiently accurate input data to make the alteration of data unnecessary. Use of this procedure could also help avoid exaggerated and misleading predictions from models.
VL  - 11
IS  - 3
ER  -

Copy | Download