2016
DOI: 10.1007/978-3-319-30634-6
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Introduction to Nonparametric Statistics for the Biological Sciences Using R

Abstract: the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific … Show more

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Cited by 153 publications
(86 citation statements)
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“…The data set for AGC with the fallow phase considered as a single factor, and the other dependent variables, all fulfilled ANOVA assumptions (one-way ANOVA). Soil carbon and AGC analyzed as three separate fallow phases, and AGC with fallow phases as a group did not meet the ANOVA assumptions and so the KruskalWallis One-Way ANOVA on Ranks was run instead, reporting the Kruskal-Wallis chi-squared approximation values (MacFarland and Yates 2016). In order to identify the treatments which are responsible for significant variation, two type of post hoc test were performed, a Tukey's HSD when distribution was normal and pairwise comparisons using Tukey and Kramer (Nemenyi) test with Tukey-Dist approximation for independent samples when the distribution was non-normal.…”
Section: Statistical Analysis Of Field Datamentioning
confidence: 99%
“…The data set for AGC with the fallow phase considered as a single factor, and the other dependent variables, all fulfilled ANOVA assumptions (one-way ANOVA). Soil carbon and AGC analyzed as three separate fallow phases, and AGC with fallow phases as a group did not meet the ANOVA assumptions and so the KruskalWallis One-Way ANOVA on Ranks was run instead, reporting the Kruskal-Wallis chi-squared approximation values (MacFarland and Yates 2016). In order to identify the treatments which are responsible for significant variation, two type of post hoc test were performed, a Tukey's HSD when distribution was normal and pairwise comparisons using Tukey and Kramer (Nemenyi) test with Tukey-Dist approximation for independent samples when the distribution was non-normal.…”
Section: Statistical Analysis Of Field Datamentioning
confidence: 99%
“…If normality was supported, one-way Analysis of Variance (ANOVA) was used to test for differences among sites. In contrast, when the data were not normally distributed, a Kruskal and Wallis test was used [23,24,25,26]. Finally, an Exact Wilcoxon Mann-Whitney Rank Sum Test was implemented to test for pairwise differences between sites.…”
Section: Discussionmentioning
confidence: 99%
“…Based on descriptive statistics (Table 2) and visual inspection of the distributions, we considered the distribution of scores on each rubric element and the total score approximately normal. While this judgment satisfies the statistical assumptions for parametric statistics, the limited outcomes on each rubric element led us to apply non-parametric statistical tests which are more appropriate for nominal data (Aron, Aron, & Coups, 2009;MacFarland & Yates, 2016). Differences between the integrated and non-integrated course on each rubric element were tested using the Mann-Whitney U test.…”
Section: Discussionmentioning
confidence: 99%