references.bib

@article{bickelSexBiasGraduate1975,
  title = {Sex Bias in Graduate Admissions: Data from Berkeley},
  author = {Bickel, P. J. and Hammel, E. A. and O'Connell J, W.},
  date = {1975-02-07},
  journaltitle = {Science},
  volume = {187},
  number = {4175},
  pages = {398--404},
  issn = {0036-8075 (Print) 0036-8075 (Linking)},
  doi = {10.1126/science.187.4175.398},
  abstract = {Examination of aggregate data on graduate admissions to the University of California, Berkeley, for fall 1973 shows a clear but misleading pattern of bias against female applicants. Examination of the disaggregated data reveals few decision-making units that show statistically significant departures from expected frequencies of female admissions, and about as many units appear to favor women as to favor men. If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. The graduate departments that are easier to enter tend to be those that require more mathematics in the undergraduate preparatory curriculum. The bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects.}
}

@article{anscombeGraphsStatisticalAnalysis1973,
  title = {Graphs in Statistical Analysis},
  author = {Anscombe, F. J.},
  date = {1973},
  journaltitle = {Am Stat},
  volume = {27},
  number = {1},
  pages = {17--21}
}

@article{weissgerberBarLineGraphs2015,
  title = {Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm},
  author = {Weissgerber, T. L. and Milic, N. M. and Winham, S. J. and Garovic, V. D.},
  date = {2015-04},
  journaltitle = {PLoS Biol},
  volume = {13},
  number = {4},
  pages = {e1002128},
  issn = {1545-7885 (Electronic) 1544-9173 (Linking)},
  doi = {10.1371/journal.pbio.1002128},
  abstract = {Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.},
  keywords = {{*Data Interpretation, Statistical},Publishing}
}

@article{rousseletReactionTimesOther2020,
  title = {Reaction Times and Other Skewed Distributions: Problems with the Mean and the Median},
  author = {Rousselet, G. A. and Wilcox, R. R.},
  date = {2020},
  journaltitle = {Meta-Psychology},
  volume = {4},
  pages = {1--39},
  doi = {10.15626/MP.2019.1630}
}

@article{labreucheDifferentsTypesVariables2010,
  title = {Les Différents Types de Variables, Leurs Représentations Graphiques et Paramètres Descriptifs},
  author = {Labreuche, Julien},
  date = {2010-12-01},
  journaltitle = {Sang Thrombose Vaisseaux},
  volume = {22},
  number = {10},
  pages = {536--543},
  issn = {0999-7385},
  doi = {10.1684/stv.2010.0541},
  abstract = {Quel que soit le phénomène étudié, on est amené à collecter et analyser un ensemble de données communément appelé en statistique « ensemble de variables ». Il~existe principalement deux groupes de variables : les variables quantitatives et les variables qualitatives, qui peuvent être subdivisés en plusieurs sous-groupes. La~reconnaissance des différents types de variables est la première étape d'une analyse de données car le choix des outils et méthodes statistiques à utiliser en dépend. Cette note aborde les différents types de variables que l'on peut rencontrer en recherche médicale. Elle décrit également les principaux graphiques (histogramme, boîte à moustaches, diagramme en bâtons, diagramme circulaire) et paramètres descriptifs (effectif, fréquence, moyenne, écart type, médiane, étendue interquartile) utilisés pour résumer l'information collectée.}
}

@article{joanesComparingMeasuresSample1998,
  title = {Comparing Measures of Sample Skewness and Kurtosis},
  author = {Joanes, D. N. and Gill, C. A.},
  date = {1998},
  journaltitle = {The Statistician},
  volume = {47},
  pages = {183--189},
  doi = {10.1111/1467-9884.00122},
  issue = {Part 1}
}

@article{hopkinsProgressiveStatisticsStudies2009,
  title = {Progressive Statistics for Studies in Sports Medicine and Exercise Science},
  author = {Hopkins, W. G. and Marshall, S. W. and Batterham, A. M. and Hanin, J.},
  date = {2009-01},
  journaltitle = {Med Sci Sports Exerc},
  volume = {41},
  number = {1},
  pages = {3--13},
  issn = {1530-0315 (Electronic) 0195-9131 (Linking)},
  doi = {10.1249/MSS.0b013e31818cb278},
  abstract = {Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.},
  keywords = {{*Data Interpretation, Statistical},*Biomedical Research,*Research Design,*Sports Medicine,Exercise/*physiology,Humans,Sample Size}
}

@article{gonzalesMeasuresCentralTendency2001,
  title = {Measures of Central Tendency in Rehabilitation Research: What Do They Mean?},
  author = {Gonzales, V. A. and Ottenbacher, K. J.},
  date = {2001-02},
  journaltitle = {Am J Phys Med Rehabil},
  volume = {80},
  number = {2},
  pages = {141--6},
  issn = {0894-9115 (Print) 0894-9115},
  doi = {10.1097/00002060-200102000-00014},
  abstract = {Measures of central tendency including the mean, median, and mode are commonly reported in rehabilitation research. It is believed that the relationship among the mean, median, and mode changes in a specific way when the distribution being analyzed is skewed. A number of widely used textbooks were reviewed to determine how the relationship among the mean, median, and mode is presented in the health sciences and rehabilitation literature. We report a potential misinterpretation of the relationship between measures of central tendency that was identified in several research and statistical textbooks on the subject of rehabilitation. The misinterpretation involves measures of central tendency derived from skewed unimodal sample distributions. The reviewed textbooks state or imply that in asymmetrical distributions, the median is always located between the mode and mean. An example is presented illustrating the fallacy of this assumption. The mean and median will always be to the right of the mode in a positively skewed unimodal distribution and to the left of the mode in a negatively skewed distribution; the order of the mean and median is impossible to predict or generalize. The assumption that the median always falls between the mode and mean in the calculation of coefficients of skewness has implications for the interpretation of exploratory and confirmatory data analysis in rehabilitation research.},
  langid = {english},
  keywords = {{*Data Interpretation, Statistical},*Rehabilitation,Humans}
}

@article{halperinSpuriousCorrelationsCauses1986,
  title = {Spurious Correlations--Causes and Cures},
  author = {Halperin, S.},
  date = {1986},
  journaltitle = {Psychoneuroendocrinology},
  volume = {11},
  number = {1},
  pages = {3--13},
  issn = {0306-4530 (Print) 0306-4530},
  doi = {10.1016/0306-4530(86)90028-4},
  abstract = {Although the Pearson Product--Moment Correlation Coefficient is one of the most widely used statistics in the health and behavioral sciences, it is not always appreciated that the critical assumption of bivariate normality underlies its interpretation. When variables have marginal distributions which are skewed or have heavy tails which produce outliers, correlations may be either spuriously large or small. Having diagnosed problems through exploratory data analysis, one must take the appropriate corrective action, such as re-expressing (transforming) variables or selectively discarding discordant observations.},
  langid = {english},
  keywords = {*Statistics as Topic,Research Design/*standards}
}

@article{grenierQuelleEstBonne2007,
  title = {Quelle Est La « Bonne » Formule de l’écart-Type ?},
  author = {Grenier, E.},
  date = {2007},
  journaltitle = {Revue MODULAD},
  volume = {37},
  pages = {102--105}
}

@article{dartCommentDecrireDistribution2003,
  title = {Comment Décrire La Distribution d'une Variable ?},
  author = {Dart, T. and Chatellier, G.},
  date = {2003},
  journaltitle = {Rev Mal Respir},
  volume = {20},
  number = {6},
  pages = {946--951},
  doi = {RMR-12-2003-20-6-0761-8425-101019-ART19}
}

@article{chatellierMoyenneMedianeLeurs2003,
  title = {Moyenne, Médiane, et Leurs Indices de Dispersion : Quand Les Utiliser et Comment Les Présenter Dans Un Article Scientifique ?},
  author = {Chatellier, G. and Durieux, P.},
  date = {2003},
  journaltitle = {Rev Mal Respir},
  volume = {20},
  number = {3},
  pages = {421--424},
  doi = {RMR-06-2003-20-3-0761-8425-101019-ART17}
}

@book{navarroLearningStatistics2018,
  title = {Learning Statistics with {{R}}},
  author = {Navarro, D.},
  date = {2018},
  publisher = {UNSW Computational Cognitive Science},
  location = {Publié en ligne},
  pagetotal = {599}
}

@book{wickhamDataScience2017,
  title = {R for {{Data Science}}},
  author = {Wickham, H. and Grolemund, G.},
  date = {2017},
  publisher = {O'Reilly},
  location = {Sebastopol, CA},
  isbn = {978-1-4919-1039-9},
  pagetotal = {492}
}

@book{wilkeFundamentalsDataVisualization2018,
  title = {Fundamentals of Data Visualization},
  author = {Wilke, C. O.},
  date = {2018},
  publisher = {O’Reilly Media, Inc. Retrieved from https://clauswilke.com/dataviz},
  location = {Sebastopol, CA},
  pagetotal = {300}
}

@book{wickhamGgplot22016,
  title = {Ggplot2},
  author = {Wickham, H.},
  date = {2016},
  edition = {2},
  publisher = {Springer-Verlag},
  location = {New York City, NY},
  pagetotal = {213}
}

@article{allenRaincloudPlotsMultiplatform2019,
  title = {Raincloud Plots: A Multi-Platform Tool for Robust Data Visualization},
  shorttitle = {Raincloud Plots},
  author = {Allen, Micah and Poggiali, Davide and Whitaker, Kirstie and Marshall, Tom Rhys and Kievit, Rogier A.},
  date = {2019-04-01},
  journaltitle = {Wellcome Open Res},
  volume = {4},
  pages = {63},
  issn = {2398-502X},
  doi = {10.12688/wellcomeopenres.15191.1},
  url = {https://wellcomeopenresearch.org/articles/4-63/v1},
  urldate = {2021-07-22},
  abstract = {Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab (               https://github.com/RainCloudPlots/RainCloudPlots               ). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.},
  langid = {english},
  file = {C:\Users\pydemullenheim\Zotero\storage\MHS64EPN\Allen et al. - 2019 - Raincloud plots a multi-platform tool for robust .pdf}
}

@article{lakensCalculatingReportingEffect2013,
  title = {Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-Tests and {{ANOVAs}}},
  shorttitle = {Calculating and Reporting Effect Sizes to Facilitate Cumulative Science},
  author = {Lakens, Daniël},
  date = {2013},
  journaltitle = {Front. Psychol.},
  volume = {4},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2013.00863},
  url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00863/abstract},
  urldate = {2023-10-20},
  file = {C:\Users\pydemullenheim\Zotero\storage\JF85NLXV\Lakens - 2013 - Calculating and reporting effect sizes to facilita.pdf}
}

@article{kelleyEffectsNonnormalDistributions2005,
  title = {The {{Effects}} of Nonnormal Distributions on Confidence {{Intervals}} around the Standardized Mean Difference: Bootstrap and Parametric Confidence Intervals},
  shorttitle = {The {{Effects}} of {{Nonnormal Distributions}} on {{Confidence Intervals Around}} the {{Standardized Mean Difference}}},
  author = {Kelley, Ken},
  date = {2005},
  journaltitle = {Educ Psychol Meas},
  volume = {65},
  number = {1},
  pages = {51--69},
  issn = {0013-1644, 1552-3888},
  doi = {10.1177/0013164404264850},
  url = {http://journals.sagepub.com/doi/10.1177/0013164404264850},
  urldate = {2023-10-21},
  abstract = {The standardized group mean difference, Cohen’s d, is among the most commonly used and intuitively appealing effect sizes for group comparisons. However, reporting this point estimate alone does not reflect the extent to which sampling error may have led to an obtained value. A confidence interval expresses the uncertainty that exists between d and the population value, δ, it represents. A set of Monte Carlo simulations was conducted to examine the integrity of a noncentral approach analogous to that given by Steiger and Fouladi, as well as two bootstrap approaches in situations in which the normality assumption is violated. Because d is positively biased, a procedure given by Hedges and Olkin is outlined, such that an unbiased estimate of δ can be obtained. The bias-corrected and accelerated bootstrap confidence interval using the unbiased estimate of δ is proposed and recommended for general use, especially in cases in which the assumption of normality may be violated.},
  langid = {english}
}

@online{janeEffectSizesConfidence2023,
  title = {Effect Sizes and Confidence Intervals Guide},
  author = {Jané, Matthew B. and Xiao, Qinyu and Yeung, Siu Kit and Dunleavy, Daniel J. and Röseler, Lukas and Elsherif, Mahmoud and Cousineau, Denis and Caldwell, Aaron R. and Johnson, Blair T. and Feldman, Gilad},
  date = {2023},
  url = {https://matthewbjane.quarto.pub/guide-to-effect-sizes-and-confidence-intervals/}
}

@article{loschiExerciseTrainingIntervention2023,
  title = {Exercise Training as an Intervention for Frailty in Cirrhotic Patients on the Liver Transplant Waiting List: {{A}} Systematic Review},
  shorttitle = {Exercise Training as an Intervention for Frailty in Cirrhotic Patients on the Liver Transplant Waiting List},
  author = {Loschi, Thais Mellato and Baccan, Melline D T A and Della Guardia, Bianca and Martins, Paulo N and Boteon, Amanda P C S and Boteon, Yuri L},
  date = {2023-10-27},
  journaltitle = {World J Hepatol},
  volume = {15},
  number = {10},
  pages = {1153--1163},
  issn = {1948-5182},
  doi = {10.4254/wjh.v15.i10.1153},
  url = {https://www.wjgnet.com/1948-5182/full/v15/i10/1153.htm},
  urldate = {2023-11-16},
  abstract = {Patients with liver cirrhosis are advised to limit their sodium consumption to control excessive fluid accumulation. Salt is the most common form in which sodium is consumed daily. Consequently, various recommendations urge patients to limit salt intake. However, there is a lack of consistency regarding salt restriction across the guidelines. Moreover, there is conflicting evidence regarding the efficacy of salt restriction in the treatment of ascites. Numerous studies have shown that there is no difference in ascites control between patients with restriction of salt intake and those without restriction. Moreover, patients with cirrhosis may have several negative effects from consuming too little salt, although there are no recommendations on the lower limit of salt intake. Sodium is necessary to maintain the extracellular fluid volume; hence, excessive salt restriction can result in volume contraction, which could negatively impact kidney function in a cirrhotic patient. Salt restriction in cirrhotic patients can also compromise nutrient intake, which can have a negative impact on the overall outcome. There is insufficient evidence to recommend restricted salt intake for all patients with cirrhosis, including those with severe hyponatremia. The existing guidelines on salt restriction do not consider the salt sensitivity of patients; their nutritional state, volume status and sodium storage sites; and the risk of hypochloremia. This opinion article aims to critically analyze the existing literature with regard to salt recommendations for patients with liver cirrhosis and identify potential knowledge gaps that call for further research.}
}

@article{liEffectSizeMeasures2016,
  title = {Effect Size Measures in a Two-Independent-Samples Case with Nonnormal and Nonhomogeneous Data},
  author = {Li, Johnson Ching-Hong},
  date = {2016-12},
  journaltitle = {Behav Res},
  volume = {48},
  number = {4},
  pages = {1560--1574},
  issn = {1554-3528},
  doi = {10.3758/s13428-015-0667-z},
  url = {http://link.springer.com/10.3758/s13428-015-0667-z},
  urldate = {2024-10-25},
  langid = {english},
  file = {C:\Users\pydemullenheim\Zotero\storage\EBUXXCPP\Li - 2016 - Effect size measures in a two-independent-samples .pdf}
}