Data Visualization and Statistical Graphics in Big Data Analysis

Cook, Dianne; Lee, Eun Kyung; Majumder, Mahbubul

doi:10.1146/annurev-statistics-041715-033420

Cited by 20 publications

(8 citation statements)

References 44 publications

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“…However, all these measures provide a single effect size estimate that may not be very informative or could even be misleading with regard to the possible complex differences between two distributions 23,35,36 . To fully represent and compare distributions, robust statistical and informatively-rich graphical methods such as the cumulative distribution function (CDF) 25 and the shift-≈ function 23,24 are required 23,25,[36][37][38][39] . Consequently, we used these methods to provide two complementary perspectives 23 of the multivariate sex differences in GMVOL.…”

Section: Resultsmentioning

confidence: 99%

Beyond “Sex Prediction”: Estimating and Interpreting Multivariate Sex Differences and Similarities in the Brain

Sanchis‐Segura

Aguirre

Cruz-Gómez

et al. 2021

Preprint

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Previous studies have shown that machine-learning (ML) algorithms can “predict” sex based on brain anatomical/ functional features. The high classification accuracy achieved by ML algorithms is often interpreted as revealing large differences between the brains of males and females and as confirming the existence of “male/female brains”. However, classification and estimation are quite different concepts, and using classification metrics as surrogate estimates of between-group differences results in major statistical and interpretative distortions. The present study illustrates these distortions and provides a novel and detailed assessment of multivariate sex differences in gray matter volume (GMVOL) that does not rely on classification metrics. Moreover, modeling and clustering techniques and analyses of similarities (ANOSIM) were used to identify the brain areas that contribute the most to these multivariate differences, and to empirically assess whether they assemble into two sex-typical profiles. Results revealed that multivariate sex differences in GMVOL: 1) are “large” if not adjusted for total intracranial volume (TIV) variation, but “small” when controlling for this variable; 2) differ in size between individuals and also depends on the ML algorithm used for their calculation 3) do not stem from two sex-typical profiles, and so describing them in terms of “male/female brains” is misleading.

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Section: Resultsmentioning

confidence: 99%

Beyond “Sex Prediction”: Estimating and Interpreting Multivariate Sex Differences and Similarities in the Brain

Sanchis‐Segura

Aguirre

Cruz-Gómez

et al. 2021

Preprint

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“…Data visualization has long been used for viewers to see patterns, trends, or anomalies ( Friendly, 2008 ) and continues to be instrumental in the era of big data ( Cook et al, 2016 ). Not all data visualization is made equal in its ability to communicate information to the viewer.…”

Section: Data Visualizationmentioning

confidence: 99%

ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1

Morota

Cheng

Cook

et al. 2021

Journal of Animal Science

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Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance human–computer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.

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“…The benefits of illustrating data distributions have been emphasised in many publications and is often the topic of one of the first chapters of introductory statistics books (Wilcox, 2006;Allen et al, 2012;Duke et al, 2015;Weissgerber et al, 2015;Cook et al, 2016). One of the most striking examples is provided by Anscombe's quartet (Anscombe, 1973), in which very different distributions, illustrated using scatterplots, are associated with the same summary statistics.…”

Section: Introductionmentioning

confidence: 99%

Beyond differences in means: robust graphical methods to compare two groups in neuroscience

Rousselet

Pernet

Wilcox

2017

Eur J of Neuroscience

182

183

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If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here, we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist's toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and MATLAB of the graphical tools, and all the examples in the article can be reproduced using R scripts.

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Data Visualization and Statistical Graphics in Big Data Analysis

Cited by 20 publications

References 44 publications

Beyond “Sex Prediction”: Estimating and Interpreting Multivariate Sex Differences and Similarities in the Brain

Beyond “Sex Prediction”: Estimating and Interpreting Multivariate Sex Differences and Similarities in the Brain

ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1

Beyond differences in means: robust graphical methods to compare two groups in neuroscience

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