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      A geochemical and mineralogical characterization of soils associated with podoconiosis

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          Abstract

          Podoconiosis is a disease that causes swelling and disfiguration of the lower legs found in several developing countries where shoes are not regularly worn. The current model for the etiology of the disease proposes that mineralogical agents enter the lymph system through the skin leading to inflammation that causes swelling of the feet and legs. We collected 125 soil samples from 21 towns associated with podoconiosis, 8 towns unassociated with Podoconiosis as controls, and 3 towns of unknown status. Data collected for each soil sample included color, particle size, mineralogy, and geochemistry to distinguish unique components within the podoconiosis-associated soils. Our results indicate podoconiosis-associated soils are more highly weathered than non-podoconiosis associated soils. The enrichment of kaolinite and gibbsite suggests that these minerals, their surface chemistry, and trace elements associated with them should be prioritized in future podoconiosis research. In addition, we found that color may be a valuable tool to identify soils at greater risk for inducing podoconiosis.

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            A farewell to Bonferroni: the problems of low statistical power and publication bias

            S Nakagawa (2004)
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              A visitor's guide to effect sizes: statistical significance versus practical (clinical) importance of research findings.

              Effect Sizes (ES) are an increasingly important index used to quantify the degree of practical significance of study results. This paper gives an introduction to the computation and interpretation of effect sizes from the perspective of the consumer of the research literature. The key points made are: 1. ES is a useful indicator of the practical (clinical) importance of research results that can be operationally defined from being "negligible" to "moderate", to "important". 2. The ES has two advantages over statistical significance testing: (a) it is independent of the size of the sample; (b) it is a scale-free index. Therefore, ES can be uniformly interpreted in different studies regardless of the sample size and the original scales of the variables. 3. Calculations of the ES are illustrated by using examples of comparisons between two means, correlation coefficients, chi-square tests and two proportions, along with appropriate formulas. 4. Operational definitions for the ES s are given, along with numerical examples for the purpose of illustration.
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                Author and article information

                Contributors
                jnhiday@gmail.com
                Journal
                Environ Geochem Health
                Environ Geochem Health
                Environmental Geochemistry and Health
                Springer Netherlands (Dordrecht )
                0269-4042
                1573-2983
                15 July 2023
                15 July 2023
                2023
                : 45
                : 11
                : 7791-7812
                Affiliations
                Department of Earth and Biological Sciences, Loma Linda University, ( https://ror.org/04bj28v14) Loma Linda, CA 92350 USA
                Article
                1625
                10.1007/s10653-023-01625-5
                10611848
                37452931
                9b1378f6-13c4-4a9a-9229-8212d9fd2ec2
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 December 2022
                : 16 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008891, Loma Linda University;
                Funded by: Loma Linda University
                Categories
                Original Paper
                Custom metadata
                © Springer Nature B.V. 2023

                ethiopia,weathering,multivariate statistics,etiology,tropical disease,silica

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