Improving Research through Avoiding Common Statistical Errors: The Case of Piosphere

Shahriary, Eahsan and Gill, Thomas E. and Langford, Richard P. and Hussein, Musa and Hargrove, William L. and Golding, Peter (2020) Improving Research through Avoiding Common Statistical Errors: The Case of Piosphere. Asian Journal of Probability and Statistics, 10 (2). pp. 48-58. ISSN 2582-0230

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Abstract

Improving Research through Avoiding Common Statistical Errors: The Case of Piosphere Eahsan Shahriary Thomas E. Gill Richard P. Langford Musa Hussein William L. Hargrove Peter Golding

For many years scientists studied the piosphere concept- a grazing gradient around a natural/artificial watering point. As is the case for other kinds of ecological studies, the method of statistical analyses applied in many publications is not always appropriate. We note there are many statistical errors and misapplication of data analysis techniques. We reviewed 875 piosphere-related publications between 1915-2018 to find the common statistical methods and common statistical errors in the design of the study, data analyses, presentation of results, and interpretation of study findings. One-way ANOVA, multiple linear regression, Pearson correlation coefficient, permutational multivariate analysis of variance, canonical correspondence analysis, and mean were the most frequent statistical methods applied. Seventy-one common statistical errors in piosphere publications were found. The most common errors were not choosing the proper or appropriate statistical techniques, not checking the assumptions and diagnostics of statistical methods, partial and wrong interpretation of results, and not using informative figures and tables to help readers. Negligence to the proper application of statistics by researchers results in inaccurate interpretation and spurious conclusions. It is recommended researchers seek advice from statisticians at the early stages of research to save resources, time, and labor and to provide increased trust in recommendations and findings.
12 18 2020 48 58 10.9734/ajpas/2020/v10i230244 https://journalajpas.com/index.php/AJPAS/article/view/202 https://www.journalajpas.com/index.php/AJPAS/article/download/30244/56747 https://www.journalajpas.com/index.php/AJPAS/article/download/30244/56747 https://www.journalajpas.com/index.php/AJPAS/article/download/30244/56748

Item Type: Article
Subjects: STM One > Mathematical Science
Depositing User: Unnamed user with email support@stmone.org
Date Deposited: 15 Mar 2023 07:09
Last Modified: 05 Sep 2024 11:14
URI: http://publications.openuniversitystm.com/id/eprint/536

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