Journal ArticleDOI
A Note on the Influence of Outliers on Parametric and Nonparametric Tests
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TLDR
In this paper, the authors show that extreme deviant scores, or outliers, reduce the probability of Type I errors of the Student t test and, at the same time, substantially increase the probability for Type II errors, so that power declines.Abstract:
Extremely deviant scores, or outliers, reduce the probability of Type I errors of the Student t test and, at the same time, substantially increase the probability of Type II errors, so that power declines. The magnitude of the change depends jointly on the probability of occurrence of an outlier and its extremity, or its distance from the mean. Although outliers do not modify the probability of Type I errors of the Mann-Whitney-Wilcoxon test, they nevertheless increase the probability of Type II errors and reduce power. The effect on this nonparametric test depends largely on the probability of occurrence and not the extremity. Because deviant scores influence the t test to a relatively greater extent, the nonparametric method acquires an advantage for outlier-prone densities despite its loss of power.read more
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Experimental Design and Data Analysis for Biologists
Gerry P. Quinn,Michael J. Keough +1 more
TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Journal ArticleDOI
The power of outliers (and why researchers should ALWAYS check for them)
Jason W. Osborne,Amy Overbay +1 more
TL;DR: The goal of this paper is to summarize the various potential causes of extreme scores in a data set, how to detect them, and whether they should be removed or not, and how significantly a small proportion of outliers can affect even simple analyses.
Journal ArticleDOI
Improving your data transformations: Applying the Box-Cox transformation
TL;DR: The Box-Cox transformation (Box & Cox, 1964) as mentioned in this paper is a family of power transformations that incorporates and extends the traditional options to help researchers easily find the optimal normalizing transformation for each variable.
Journal ArticleDOI
Ten ironic rules for non-statistical reviewers.
TL;DR: A series of critiques that can be applied universally to any neuroimaging paper are reviewed and responses to potential rebuttals that reviewers might encounter from authors or editors are considered.
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Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute☆
TL;DR: Latent Class Analysis method as a clustering approach of educational data mining was employed to extract common activity features of 612 courses in a large private university located in South Korea by using online behavior data tracked from Learning Management System and institution's course database.
References
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Book
Outliers in Statistical Data
Vic Barnett,Toby Lewis +1 more
TL;DR: In this article, the authors present an updated version of the reference work on outliers, including new areas of study such as outliers in direction data as well as developments in fields such as discordancy tests for univariate and multivariate samples.
Book
Robust statistics: the approach based on influence functions
TL;DR: This paper presents a meta-modelling framework for estimating the values of Covariance Matrices and Multivariate Location using one-Dimensional and Multidimensional Estimators.
Journal ArticleDOI
Rank Transformations as a Bridge between Parametric and Nonparametric Statistics
TL;DR: Rank as mentioned in this paper is a nonparametric procedure that is applied to the ranks of the data instead of to the data themselves, and it can be viewed as a useful tool for developing non-parametric procedures to solve new problems.
Journal ArticleDOI
A Note on the Generation of Random Normal Deviates
Book
Identification of outliers
TL;DR: A computer normalizes the one or more sets of historical data points and creates a first visual representation corresponding to the first set of the oneor more sets and the second set of additional points.