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Managing the Assumption of Normality within the General Linear Model with Small Samples: Guidelines for Researchers Regarding If, When and How. [PDF]
Tutorials in Quantitative Methods for Psychology, 2023 Academic textbooks, statistical literature, and publication guidelines provide conflicting, ambiguous and often incomplete answers to the question of how researchers should handle the normality assumption for classical general linear model tests when ...
Zygmont, Conrad Stanisław
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Ensemble Based Box-Cox Transformation via Meta Analysis
Journal of Advanced Research in Natural and Applied Sciences, 2022 Normal distribution has a vital role for the most of statistical methods. Box-Cox power transformation is the most usually applied method when the distribution of data is not normal.
Osman Dağ, Muhammed Ali Yılmaz
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Fibers, 2022 The ACI 544-2R repeated impact test is known as a low-cost and simple qualitative test to evaluate the impact strength of concrete. However, the test’s main deficiency is the high variability in its results. The effect of steel fibers and the compressive
Ahmmad A. Abbass +3 more
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Frontiers in Neuroinformatics, 2023 In the analysis of neural data, measures of non-Gaussianity are generally applied in two ways: as tests of normality for validating model assumptions and as Independent Component Analysis (ICA) contrast functions for separating non-Gaussian signals ...
Parul Jain +4 more
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Normality assessment, few paradigms and use cases
Romanian Journal of Laboratory Medicine, 2022 Background: The importance of applying the normality tests is underlined by the way of continuing the statistical protocol for numerical data within inferential statistics, respectively by the parametric or non-parametric tests that we will apply further
Avram Călin, Mărușteri Marius
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Quality Control and Homogeneity Analysis of Precipitation Time Series in the Climatic Region of Iraq
Atmosphere, 2023 Non-climatic reasons, such as station replacement and changing the measurement device and calculation method, may make climate data unrepresentative of the actual variation of the regional climate.
Ruqayah Mohammed, Miklas Scholz
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Testing for normality with neural networks [PDF]
Neural Computing and Applications, 2021 In this paper, we treat the problem of testing for normality as a binary classification problem and construct a feedforward neural network that can successfully detect normal distributions by inspecting small samples from them. The numerical experiments conducted on small samples with no more than 100 elements indicated that the neural network which we
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New fat-tail normality test based on conditional second moments with applications to finance [PDF]
, 2020 In this paper we introduce an efficient fat-tail measurement framework that is based on the conditional second moments. We construct a goodness-of-fit statistic that has a direct interpretation and can be used to assess the impact of fat-tails on central
Jelito, Damian, Pitera, Marcin
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Parametric or Non-parametric: Skewness to Test Normality for Mean Comparison
International Journal of Assessment Tools in Education, 2020 Checking the normality assumption is necessary to decide whether a parametric or non-parametric test needs to be used. Different ways are suggested in literature to use for checking normality. Skewness and kurtosis values are one of them.
Fatih Orcan
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Quantile-Zone Based Approach to Normality Testing
Mathematics, 2022 Normality testing remains an important issue for researchers, despite many solutions that have been published and in use for a long time. There is a need for testing normality in many areas of research and application, among them in Quality control, or ...
Atif Avdović, Vesna Jevremović
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