📉Graphical Representation: Myths (Just the Quotes)

"The simplicity of the process behavior chart can be deceptive. This is because the simplicity of the charts is based on a completely different concept of data analysis than that which is used for the analysis of experimental data. When someone does not understand the conceptual basis for process behavior charts they are likely to view the simplicity of the charts as something that needs to be fixed. Out of these urges to fix the charts all kinds of myths have sprung up resulting in various levels of complexity and obstacles to the use of one of the most powerful analysis techniques ever invented." (Donald J Wheeler, "Myths About Data Analysis", International Lean & Six Sigma Conference, 2012)

"The search for better numbers, like the quest for new technologies to improve our lives, is certainly worthwhile. But the belief that a few simple numbers, a few basic averages, can capture the multifaceted nature of national and global economic systems is a myth. Rather than seeking new simple numbers to replace our old simple numbers, we need to tap into both the power of our information age and our ability to construct our own maps of the world to answer the questions we need answering." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"Clarity is related to two other principles of good data presentation: precision and efficiency. Precision refers to ensuring that the data are presented accurately with minimal error. This is a topic that is equally important to data presentation as it is to data management. Always keep in mind: don’t mislead the audience. As already mentioned, people can be fooled by visual images, but they can also be misled by the myth of the infallible graphic. This refers to a tendency to believe there is an important association among concepts simply because they are correlated." (John Hoffmann, "Principles of Data Management and Presentation", 2017)

"The first myth is that prediction is always based on time-series extrapolation into the future (also known as forecasting). This is not the case: predictive analytics can be applied to generate any type of unknown data, including past and present. In addition, prediction can be applied to non-temporal (time-based) use cases such as disease progression modeling, human relationship modeling, and sentiment analysis for medication adherence, etc. The second myth is that predictive analytics is a guarantor of what will happen in the future. This also is not the case: predictive analytics, due to the nature of the insights they create, are probabilistic and not deterministic. As a result, predictive analytics will not be able to ensure certainty of outcomes." (Prashant Natarajan et al, "Demystifying Big Data and Machine Learning for Healthcare", 2017)

"One of the biggest myths is the belief that data science is an autonomous process that we can let loose on our data to find the answers to our problems. In reality, data science requires skilled human oversight throughout the different stages of the process. [...] The second big myth of data science is that every data science project needs big data and needs to use deep learning. In general, having more data helps, but having the right data is the more important requirement. [...] A third data science myth is that modern data science software is easy to use, and so data science is easy to do. [...] The last myth about data science [...] is the belief that data science pays for itself quickly. The truth of this belief depends on the context of the organization. Adopting data science can require significant investment in terms of developing data infrastructure and hiring staff with data science expertise. Furthermore, data science will not give positive results on every project." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"Another myth is that we shall have a single source of truth for each concept or entity. […] This is a wonderful idea, and is placed to prevent multiple copies of out-of-date and untrustworthy data. But in reality it’s proved costly, an impediment to scale and speed, or simply unachievable. Data Mesh does not enforce the idea of one source of truth. However, it places multiple practices in place that reduces the likelihood of multiple copies of out-of-date data." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"I think sometimes organizations are looking at tools or the mythical and elusive data driven culture to be the strategy. Let me emphasize now: culture and tools are not strategies; they are enabling pieces." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"In the world of data and analytics, people get enamored by the nice, shiny object. We are pulled around by the wind of the latest technology, but in so doing we are pulled away from the sound and intelligent path that can lead us to data and analytical success. The data and analytical world is full of examples of overhyped technology or processes, thinking this thing will solve all of the data and analytical needs for an individual or organization. Such topics include big data or data science. These two were pushed into our minds and down our throats so incessantly over the past decade that they are somewhat of a myth, or people finally saw the light. In reality, both have a place and do matter, but they are not the only solution to your data and analytical needs. Unfortunately, though, organizations bit into them, thinking they would solve everything, and were left at the alter, if you will, when it came time for the marriage of data and analytical success with tools." (Jordan Morrow, "Be Data Literate: The data literacy skills everyone needs to succeed", 2021)

"Data generation is part of knowledge production. It is the generation of material that can help frame a debate or dispel a myth. As designers of visualizations, we choose what data and which stories are amplified through our work. As critical visualization designers, we assume a responsibility for producing those stories and for the ways they were produced." (Peter A Hall & Patricio Dávila, "Critical Visualization: Rethinking the Representation of Data", 2022)

📉Graphical Representation: Designers (Just the Quotes)

"The numerous design possibilities include several varieties of line graphs that are geared to particular types of problems. The design of a graph should be adapted to the type of data being structured. The data might be percentages, index numbers, frequency distributions, probability distributions, rates of change, numbers of dollars, and so on. Consequently, the designer must be prepared to structure his graph accordingly." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Because ease of use is the purpose, this ratio of function to conceptual complexity is the ultimate test of system design. Neither function alone nor simplicity alone defines a good design. [...] Function, and not simplicity, has always been the measure of excellence for its designers." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975)

"The interior decoration of graphics generates a lot of ink that does not tell the viewer anything new. The purpose of decoration varies - to make the graphic appear more scientific and precise, to enliven the display, to give the designer an opportunity to exercise artistic skills. Regardless of its cause, it is all non-data-ink or redundant data-ink, and it is often chartjunk."  (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Good design protects you from the need for too many highly accurate components in the system. But such design principles are still, to this date, ill-understood and need to be researched extensively. Not that good designers do not understand this intuitively, merely it is not easily incorporated into the design methods you were taught in school. Good minds are still needed in spite of all the computing tools we have developed." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)

"There is no end to the information we can use. A 'good' map provides the information we need for a particular purpose - or the information the mapmaker wants us to have. To guide us, a map’s designers must consider more than content and projection; any single map involves hundreds of decisions about presentation." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"For a given dataset there is not a great deal of advice which can be given on content and context. hose who know their own data should know best for their specific purposes. It is advisable to think hard about what should be shown and to check with others if the graphic makes the desired impression. Design should be let to designers, though some basic guidelines should be followed: consistency is important (sets of graphics should be in similar style and use equivalent scaling); proximity is helpful (place graphics on the same page, or on the facing page, of any text that refers to them); and layout should be checked (graphics should be neither too small nor too large and be attractively positioned relative to the whole page or display)." (Antony Unwin, "Good Graphics?" [in "Handbook of Data Visualization"], 2008)

"The main goal of data visualization is its ability to visualize data, communicating information clearly and effectively. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex dataset by communicating its key aspects in a more intuitive way. Yet designers often tend to discard the balance between design and function, creating gorgeous data visualizations which fail to serve its main purpose - communicate information." (Vitaly Friedman, "Data Visualization and Infographics", Smashing Magazine, 2008)

"Designers are responsible for the project’s fit and finish, that is, specifying the geometry and sizes of components so they properly mate with each other and are ergonomically and aesthetically acceptable within the operating environment." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Having a purposeless or poorly performing dashboard is more common than not. This happens when the underlying architecture is not designed properly to support the needs of dashboard interaction. There is an obvious disconnect between the design of the data warehouse and the design of the dashboards. The people who design the data warehouse do not know what the dashboard will do; and the people who design the dashboards do not know how the data warehouse was designed, resulting in a lack of cohesion between the two. A similar disconnect can also exist between the dashboard designer and the business analyst, resulting in a dashboard that may look beautiful and dazzling but brings very little business value." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"Be aware that bar charts provide ample opportunities for chart junk. The space within the bars is enticingly empty and it is tempting to put images or textures in the background. Some designers even swap out the standard bars for graphics." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"All sorts of metaphorical interpretations are culturally ingrained. An astute designer will think about these possible interpretations and work with them, rather than against them." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"A persuasive visualization primarily serves the relationship between the designer and the reader. It is useful when the designer wishes to change the reader’s mind about something. It represents a very specific point of view, and advocates a change of opinion or action on the part of the reader. In this category of visualization, the data represented is specifically chosen for the purpose of supporting the designer’s point of view, and is presented carefully so as to convince the reader of same." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"[...] visual art, primarily serves the relationship between the designer and the data. [...] it often entails unidirectional encoding of information, meaning that the reader may not be able to decode the visual presentation to understand the underlying information. [...] visual art merely translates the data into a visual form. The designer may intend only to condense it, translate it into a new medium, or make it beautiful; she may not intend for the reader to be able to extract anything from it other than enjoyment." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Information design, when successful - whether in print, on the web, or in the environment - represents the functional balance of the meaning of the information, the skills and inclinations of the designer, and the perceptions, education, experience, and needs of the audience." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"Good design is an important part of any visualization, while decoration (or chart-junk) is best omitted. Statisticians should also be careful about comparing themselves to artists and designers; our goals are so different that we will fare poorly in comparison." (Hadley Wickham, "Graphical Criticism: Some Historical Notes", Journal of Computational and Graphical Statistics Vol. 22(1), 2013)

"Developing a clear understanding of the requirements of a particular target audience is a tricky problem for a designer. While it might seem obvious to you that it would be a good idea to understand requirements, it’s a common pitfall for designers to cut corners by making assumptions rather than actually engaging with any target users. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"Usually, diagrams contain some noise - information unrelated to the diagram’s primary goal. Noise is decorations, redundant, and irrelevant data, unnecessarily emphasized and ambiguous icons, symbols, lines, grids, or labels. Every unnecessary element draws attention away from the central idea that the designer is trying to share. Noise reduces clarity by hiding useful information in a fog of useless data. You may quickly identify noise elements if you can remove them from the diagram or make them less intense and attractive without compromising the function." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"Another problem is that while data visualizations may appear to be objective, the designer has a great deal of control over the message a graphic conveys. Even using accurate data, a designer can manipulate how those data make us feel. She can create the illusion of a correlation where none exists, or make a small difference between groups look big." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Data generation is part of knowledge production. It is the generation of material that can help frame a debate or dispel a myth. As designers of visualizations, we choose what data and which stories are amplified through our work. As critical visualization designers, we assume a responsibility for producing those stories and for the ways they were produced." (Peter A Hall & Patricio Dávila, "Critical Visualization: Rethinking the Representation of Data", 2022)

"[...]  the production of data is an important aspect of critical practice precisely because of the lack of attention normally accorded to it by designers. What is made invisible, in this case, the production of data, is often one of the most important aspects for a critical thinker to focus on. What becomes standardized, routine and efficient has become naturalized, and therefore unquestioned. But it is there, in the unquestioned areas, that so many ideologically, politically, culturally framed decisions have been made." (Peter A Hall & Patricio Dávila, "Critical Visualization: Rethinking the Representation of Data", 2022)

📉Graphical Representation: Dendrogram (Just the Quotes

"Because the cluster solutions grow tree-like (starting with the branches and ending with the trunk) results are often displayed in a graphic called the dendrogram. Horizontal lines indicate linking of two samples or clusters, and thus the vertical axis presents the associated height or similarity as a measure of distance. The samples are arranged in such a way that the branches of the tree do not overlap. Linking of two groups at a large height indicates strong dissimilarity (and vice versa). Therefore, a clear cluster structure would be indicated if observations are linked at a very low height, and the distinct clusters are linked at a greater height (long branches of the tree). Cutting the dendrogram at such a greater height permits assigning the samples to the resulting distinct clusters. Visual inspection of a dendrogram is often helpful in obtaining an initial estimate of the number of clusters for partitioning methods." (Clemens Reimann et al, "Statistical Data Analysis Explained: Applied Environmental Statistics with R", 2008)

"One useful thing to do once you have clustered something hierarchically is to view the dendrogram of the partition. A dendrogram produces a tree-like diagram that shows how the clusters separate as you move further down the hierarchy." (Drew Conway & John Myles White, "Machine Learning for Hackers", 2012)

"Hierarchical clustering creates a hierarchy of clusters which can be represented in a treelike diagram, called a dendrogram. In the dendrogram, units in the same cluster are joined by a horizontal line, with the scale on the y-axis of the dendrogram reflecting a measure of the distances of the units within the cluster. The leaves at the bottom of the dendrogram represent the individual units; leaves are combined to form small branches, small branches are combined into larger branches, until one reaches the trunk or root of the tree that represents a single cluster containing all units. Dendrograms are quite useful as they give us a visual representation of the clusters." (Johannes Ledolter, "Data mining and business analytics with R", 2013)

"Different joining/linkage rules change how the final hierarchical clustering is presented. [...] Since the barrier for merging observations and clusters is lowest with the single linkage approach, the clustering dendrogram may contain chains of clusters as well as clusters that are spread out. The barrier to joining clusters is highest with complete linkage; however, it is possible that an observation is closer to observations in other clusters than the cluster to which it has been assigned. The average linkage approach moderates the tendencies of the single or complete linkage approaches." (Glenn J Myatt & Wayne P Johnson. "Making sense of data I: a practical guide to exploratory data analysis and data mining" 2nd. Ed., 2014)

"Horizontal trees have proved highly efficient for archetypal models such as classification trees, flow charts, mind maps, dendrograms, and, notably, in the display of files on several software applications and operating systems. If you are a computer user, there is a strong chance you have interacted with some version of a horizontal tree - perhaps on a daily basis." (Manuel Lima, "The Book of Trees: Visualizing Branches of Knowledge", 2014)

"Determining the optimal number of clusters in a data set is a fundamental issue in partitioning clustering, such as k-means clustering, which requires the user to specify the number of clusters k to be generated. Unfortunately, there is no definitive answer to this question. The optimal number of clusters is somehow subjective and depends on the method used for measuring similarities and the parameters used for partitioning. A simple and popular solution consists of inspecting the dendrogram produced using hierarchical clustering to see if it suggests a particular number of clusters. Unfortunately, this approach is also subjective." (Alboukadel Kassambara, "Practical Guide To  Cluster Analysis in R: Unsupervised Machine Learning", 2016)

"The dendrogram is a multilevel hierarchy where clusters at one level are joined together to form the clusters at the next levels. This makes it possible to decide the level at which to cut the tree for generating suitable groups of a data objects." (Alboukadel Kassambara, "Practical Guide To  Cluster Analysis in R: Unsupervised Machine Learning", 2016)

"Agglomerative clustering produces what is known as a hierarchical clustering. The clustering proceeds iteratively, and every point makes a journey from being a single point cluster to belonging to some final cluster. Each intermediate step provides a clustering of the data (with a different number of clusters). It is sometimes helpful to look at all possible clusterings jointly. [...] While this visualization provides a very detailed view of the hierarchical clustering, it relies on the two-dimensional nature of the data and therefore cannot be used on datasets that have more than two features. There is, however, another tool to visualize hierarchical clustering, called a dendrogram, that can handle multidimensional datasets." (Andreas C Müller & Sarah Guido, "Introduction to Machine Learning with Python: A Guide for Data Scientists", 2017)

"Hierarchical clustering is comprised of a general family of clustering algorithms that construct nested clusters by successive merging or splitting of data. The hierarchy of clusters is represented as a tree. The tree is usually called a dendrogram. The root of the dendrogram is the single cluster that contains all the samples; the leaves are the clusters containing only one sample each. This is a nice tool, since it can be straightforwardly interpreted: it 'explains' how clusters are formed and visualizes clusters at different scales. The tree that results from the technique shows the similarity between the samples. Partitioning is computed by selecting a cut on the tree at a certain level." (Laura Igual & Santi Seguí, "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications", 2017)

"The dendrogram provides a visual representation of the relatedness of variables within a cluster. The height of the horizontal lines indicates the degree of difference between branches. The longer the line, the greater the difference." (Richard V. McCarthy et al, "Applying Predictive Analytics: Finding Value in Data", 2019)

"A tree-like structure or a tree diagram, one showing taxonomic (classification-related) relationships, is created. It is known as a dendrogram. It has branches pointing toward categories or classes. It is a pattern formed by a series of splits or segments of a given quantity of data over a set of compartments and the flow components. From this pattern, a description can be made of the profile of data allocation over the set of functional or structural compartments. The merger of clusters is terminated after every data point lies in one single cluster at the top of the tree." (Vinod K Khanna, "Introduction to Machine Learning Algorithms:  Basic Principles and Mathematics", 2026)

"Hierarchical clustering is summarised by a dendrogram, which sequentially shows points being joined to form a cluster, with the corresponding distances. Breaking the data into clusters is done by cutting the dendrogram at the long edges. [...] Plotting the dendrogram in the data space can help you understand how the hierarchical clustering has collected the points together into clusters. You can learn if the algorithm has been confused by nuisance patterns in the data, and how different choices of linkage method affect the result." (Dianne Cook & Ursula Laa, "Interactively Exploring High-Dimensional Data and Models in R", 2026)

"Viewing the dendrograms in high dimensions provides insight into how the algorithm has joined points to clusters. For example, single linkage often has edges leading to a single focal point, which might not yield a useful clustering but might help to identify outliers. If the edges point to multiple focal points, with long edges bridging gaps in the data, the result is more likely yielding a useful clustering." (Dianne Cook & Ursula Laa,  "Interactively Exploring High-Dimensional Data and Models in R", 2026)

📉Graphical Representation: Composition (Just the Quotes)

"Nothing is so illuminating as a set of properly proportioned diagrams. [...] In addition to the significance of graphics in analytical work, it is likewise a valuable aid to the memory. A picture is manifestly more readily retained in mind than a description of the same subject, no matter how vividly it may have been expressed. A pictorial or diagrammatic illustration usually produces a firmer and more lasting impression than any composition of words or tabulation of figures, however well they may be arranged or set forth." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Without adequate planning, it is seldom possible to achieve either proper emphasis of each component element within the chart or a presentation that is pleasing in its entirely. Too often charts are developed around a single detail without sufficient regard for the work as a whole. Good chart design requires consideration of these four major factors:" (1) size," (2) proportion," (3) position and margins, and" (4) composition." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"As a general rule, plotted points and graph lines should be given more 'weight' than the axes. In this way the 'meat' will be easily distinguishable from the 'bones'. Furthermore, an illustration composed of lines of unequal weights is always more attractive than one in which all the lines are of uniform thickness. It may not always be possible to emphasise the data in this way however. In a scattergram, for example, the more plotted points there are, the smaller they may need to be and this will give them a lighter appearance. Similarly, the more curves there are on a graph, the thinner the lines may need to be. In both cases, the axes may look better if they are drawn with a somewhat bolder line so that they are easily distinguishable from the data." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"Functional visualizations are more than innovative statistical analyses and computational algorithms. They must make sense to the user and require a visual language system that uses color, shape, line, hierarchy and composition to communicate clearly and appropriately, much like the alphabetic and character-based languages used worldwide between humans." (Matt Woolman, "Digital Information Graphics", 2002)

"While visuals are an essential part of data storytelling, data visualizations can serve a variety of purposes from analysis to communication to even art. Most data charts are designed to disseminate information in a visual manner. Only a subset of data compositions is focused on presenting specific insights as opposed to just general information. When most data compositions combine both visualizations and text, it can be difficult to discern whether a particular scenario falls into the realm of data storytelling or not." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"A semantic approach to visualization focuses on the interplay between charts, not just the selection of charts themselves. The approach unites the structural content of charts with the context and knowledge of those interacting with the composition. It avoids undue and excessive repetition by instead using referential devices, such as filtering or providing detail-on-demand. A cohesive analytical conversation also builds guardrails to keep users from derailing from the conversation or finding themselves lost without context. Functional aesthetics around color, sequence, style, use of space, alignment, framing, and other visual encodings can affect how users follow the script." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Aligning on data ink can be a powerful way to build relationships across charts. It can be used to obscure the lines between charts, making the composition feel more seamless. [....] Alignment paradigms can also influence the layout design needed. [...] The layout added to the alignment further supports this relationship." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Beyond basic charts, practitioners must also learn to compose visualizations together elegantly. The perceptual stage focuses on making the literal charts more precise as well as working to de-emphasize the entire piece. Design choices start to consider distractions, reducing visual clutter and centering on the message. Minimalism is espoused as a core value with an emphasis on shifting toward precision as accuracy. This is the most common next step for practitioners. Minimalism is also a key stage in maturation. It is experimentation at one extreme that helps practitioners distill down to core, shared practices." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Chart choices can also create weight within the entire composition. Presenting information as a comprehensive visualization, such as in a dashboard, requires thinking beyond individual charts. In writing, we not only craft sentences, but write the composition as an entire piece. Certain sentences may drive the writing more, but all sentences play a role in conveying the message." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Visualizations are abstractions, relying on primary graphicacy skills to fully understand the composition." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

📉Graphical Representation: Distribution (Just the Quotes)

"Some distributions [...] are symmetrical about their central value. Other distributions have marked asymmetry and are said to be skew. Skew distributions are divided into two types. If the 'tail' of the distribution reaches out into the larger values of the variate, the distribution is said to show positive skewness; if the tail extends towards the smaller values of the variate, the distribution is called negatively skew." (Michael J Moroney, "Facts from Figures", 1951)

"The impression created by a chart depends to a great extent on the shape of the grid and the distribution of time and amount scales. When your individual figures are a part of a series make sure your own will harmonize with the other illustrations in spacing of grid rulings, lettering, intensity of lines, and planned to take the same reduction by following the general style of the presentation." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"The logarithmic transformation serves several purposes:" (1) The resulting regression coefficients sometimes have a more useful theoretical interpretation compared to a regression based on unlogged variables. (2) Badly skewed distributions - in which many of the observations are clustered together combined with a few outlying values on the scale of measurement - are transformed by taking the logarithm of the measurements so that the clustered values are spread out and the large values pulled in more toward the middle of the distribution. (3) Some of the assumptions underlying the regression model and the associated significance tests are better met when the logarithm of the measured variables is taken." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Plotting on power-transformed scales (either cube roots or logs) is recommended only in those cases where the distribution is very asymmetric and the reference configuration for the untransformed plot would be a straight line through the origin." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Boxplots provide information at a glance about center (median), spread (interquartile range), symmetry, and outliers. With practice they are easy to read and are especially useful for quick comparisons of two or more distributions. Sometimes unexpected features such as outliers, skew, or differences in spread are made obvious by boxplots but might otherwise go unnoticed." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Comparing normal distributions reduces to comparing only means and standard deviations. If standard deviations are the same, the task even simpler: just compare means. On the other hand, means and standard deviations may be incomplete or misleading as summaries for nonnormal distributions." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"If a distribution were perfectly symmetrical, all symmetry-plot points would be on the diagonal line. Off-line points indicate asymmetry. Points fall above the line when distance above the median is greater than corresponding distance below the median. A consistent run of above-the-line points indicates positive skew; a run of below-the-line points indicates negative skew." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Remember that normality and symmetry are not the same thing. All normal distributions are symmetrical, but not all symmetrical distributions are normal. With water use we were able to transform the distribution to be approximately symmetrical and normal, but often symmetry is the most we can hope for. For practical purposes, symmetry (with no severe outliers) may be sufficient. Transformations are not a magic wand, however. Many distributions cannot even be made symmetrical." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Many good things happen when data distributions are well approximated by the normal. First, the question of whether the shifts among the distributions are additive becomes the question of whether the distributions have the same standard deviation; if so, the shifts are additive. […] A second good happening is that methods of fitting and methods of probabilistic inference, to be taken up shortly, are typically simple and on well understood ground. […] A third good thing is that the description of the data distribution is more parsimonious." (William S Cleveland, "Visualizing Data", 1993)

"The quantile plot is a good general display since it is fairly easy to construct and does a good job of portraying many aspects of a distribution. Three convenient features of the plot are the following: First, in constructing it, we do not make any arbitrary choices of parameter values or cell boundaries [...] and no models for the data are fitted or assumed. Second, like a table, it is not a summary but a display of all the data. Third, on the quantile plot every point is plotted at a distinct location, even if there are duplicates in the data. The number of points that can be portrayed without overlap is limited only by the resolution of the plotting device. For a high resolution device several hundred points distinguished." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Boxplots provide information at a glance about center (median), spread (interquartile range), symmetry, and outliers. With practice they are easy to read and are especially useful for quick comparisons of two or more distributions. Sometimes unexpected features such as outliers, skew, or differences in spread are made obvious by boxplots but might otherwise go unnoticed." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"A useful feature of a stem plot is that the values maintain their natural order, while at the same time they are laid out in a way that emphasizes the overall distribution of where the values are concentrated (that is, where the longer branches are). This enables you easily to pick out key values such as the median and quartiles." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Histograms use area to represent counts of a distribution. This makes them somewhat related to barcharts and mosaic plots, although the number or the width of the bins of a histogram is not determined a priori and the bins are drawn without gaps between them reflecting the continuous scale of the data. Whereas barcharts and mosaic plots show the exact distribution of the sample, a histogram is always just one approximation to the distribution of the data. Sometimes histograms are also used as crude density estimators for some 'true', but usually unknown, underlying distribution for the data. There are much better density estimation methods that produce smooth distribution displays." (Antony Unwin et al [in "Graphics of Large Datasets: Visualizing a Million"], 2006)

"How would a million be visualized today? If you have ever drawn a histogram or a scatterplot of a million cases, you know that it is possible, but that there are problems. The screen resolution of a computer cannot be high enough to show very small bars in the histogram, and in regions of high density the scatterplots look like black blobs with huge numbers of points piled on top of one another. (It is noteworthy - and useful - that the weaknesses of the two kinds of plot arise at opposite extremes of the distributional densities.) So what should be visualized? If the distributional form of the bulk of the data is of interest, then the histogram will be fine for one-dimensional views (and it may give some information about outliers too). If individual outliers are of interest, then the scatterplot will be pretty good (and it will give a fair bit of distributional information as well). One aim might be described as global, attempting to summarise the main structure, and the other as local, attempting to identify individual features. Ideally, both kinds of plot are needed to satisfy both aims." (Antony Unwin et al [in "Graphics of Large Datasets: Visualizing a Million"], 2006)

"When displaying information visually, there are three questions one will find useful to ask as a starting point. Firstly and most importantly, it is vital to have a clear idea about what is to be displayed; for example, is it important to demonstrate that two sets of data have different distributions or that they have different mean values? Having decided what the main message is, the next step is to examine the methods available and to select an appropriate one. Finally, once the chart or table has been constructed, it is worth reflecting upon whether what has been produced truly reflects the intended message. If not, then refine the display until satisfied; for example if a chart has been used would a table have been better or vice versa?" (Jenny Freeman et al, "How to Display Data", 2008)

"'Distribution' refers to how the vof a variable are placed along an axis, keeping the proportional distances taken from the values in the table. In descriptive statistics, there are two complementary ways to study a distribution: searching for what is common (the measures of central tendency) and searching for what is different along with how much different it is (measures of dispersion)." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"The simplest and most common way to represent the empirical distribution of a numerical variable is by showing the individual values as dots arranged along a line. The main difficulty with this plot concerns how to treat tied values. We usually don't want to represent them by the same point, since that means that the two values look like one. What we can do is 'jitter' the points a bit (i.e., move them back and forth at right angles to the plot axis) so that all points are visible. […] In addition to permitting you to identify individual points, dotplots allow you to look into some of the distributional properties of a variable. […] Dotplots can also be good for looking for modality. " (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"There is no ‘correct’ way to display sets of numbers: each of the plots we have used has some advantages: strip-charts show individual points, box-and-whisker plots are convenient for rapid visual summaries, and histograms give a good feel for the underlying shape of the data distribution." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

📉Graphical Representation: Simplification (Just the Quotes)

"Judgment must be used in the showing of figures in any chart or numerical presentation, so that the figures may not give an appearance of greater accuracy than their method of collection would warrant. Too many otherwise excellent reports contain figures which give the impression of great accuracy when in reality the figures may be only the crudest approximations. Except in financial statements, it is a safe rule to use ciphers whenever possible at the right of all numbers of great size. The use of the ciphers greatly simplifies the grasping of the figures by the reader, and, at the same time, it helps to avoid the impression of an accuracy which is not warranted by the methods of collecting the data." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919) 

"The great difference between the graphic representation of yesterday, which was poorly dissociated from the figurative image, and the graphics of tomorrow, is the disappearance of the congential fixity of the image. […] When one can superimpose, juxtapose, transpose, and permute graphic images in ways that lead to groupings and classings, the graphic image passes from the dead image, the 'illustration,' to the living image, the widely accessible research instrument it is now becoming. The graphic is no longer only the 'representation' of a final simplification, it is a point of departure for the discovery of these simplifications and the means for their justification. The graphic has become, by its manageability, an instrument for information processing." (Jacques Bertin, "Semiology of graphics" ["Semiologie Graphique"], 1967)

"What about confusing clutter? Information overload? Doesn't data have to be ‘boiled down’ and  ‘simplified’? These common questions miss the point, for the quantity of detail is an issue completely separate from the difficulty of reading. Clutter and confusion are failures of design, not attributes of information. Often the less complex and less subtle the line, the more ambiguous and less interesting is the reading. Stripping the detail out of data is a style based on personal preference and fashion, considerations utterly indifferent to substantive content." (Edward R Tufte, "Envisioning Information", 1990)

"A good chart delineates and organizes information. It communicates complex ideas, procedures, and lists of facts by simplifying, grouping, and setting and marking priorities. By spatial organization, it should lead the eye through information smoothly and efficiently." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"An axis is the ruler that establishes regular intervals for measuring information. Because it is such a widely accepted convention, it is often taken for granted and its importance overlooked. Axes may emphasize, diminish, distort, simplify, or clutter the information. They must be used carefully and accurately." (Mary H Briscoe, "Preparing Scientific Illustrations: A guide to better posters, presentations, and publications" 2nd ed., 1995)

"Good ideas do not communicate themselves. Ideas must be organized. Highly complex ideas need to be clarified and simplified whereas diffuse data may benefit from being combined. Ideas and data must be made interesting and comprehensible to those not familiar with them." (Mary H Briscoe, "Preparing Scientific Illustrations:  guide to better posters, presentations, and publications" 2nd ed., 1995)

"Mathematical models are continually invoking ideas of infinitely smooth surfaces, weightless strings, weightless beams, perfectly spherical balls, projectiles flying through airless space, gases which are perfectly compressible and liquids which are perfectly incompressible, and so on. The purpose of such simplifications is, in theory, to understand the world better despite the oversimplification, which you hope either will not matter or will be corrected when you construct a second (better) model." (David Wells, "You Are a Mathematician: A wise and witty introduction to the joy of numbers", 1995)

"Charts are used to represent quantitative data in a graphic format. A chart visually illustrates relationships between numbers. When creating a chart, keep in mind that the goal is to represent the data in a simplified and appealing way so as not to muddle the message the chart is meant to convey." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Information graphics are an essential component of technical communication. Very few technical documents or presentations can be considered complete without graphical elements to present some essential data. Because engineers are visually oriented, graphic aids allow their thoughts and ideas to be better understood by other engineers. Information graphics are essential in presenting data because they simplify the content, offer a visually pleasing alternative to gray text in a proposal or an article, and thereby invite interest." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"The data is a simplification - an abstraction - of the real world. So when you visualize data, you visualize an abstraction of the world, or at least some tiny facet of it. Visualization is an abstraction of data, so in the end, you end up with an abstraction of an abstraction, which creates an interesting challenge. […] Just like what it represents, data can be complex with variability and uncertainty, but consider it all in the right context, and it starts to make sense." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Form simplification means simplifying relationships among the components of the whole, emphasizing the whole and reducing the relevance of individual components by standardizing and generalizing relationships. This results in an increased weight of useful information (signal) against useless information (noise)." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"GIGO is a famous saying coined by early computer scientists: garbage in, garbage out. At the time, people would blindly put their trust into anything a computer output indicated because the output had the illusion of precision and certainty. If a statistic is composed of a series of poorly defined measures, guesses, misunderstandings, oversimplifications, mismeasurements, or flawed estimates, the resulting conclusion will be flawed." (Daniel J Levitin, "Weaponized Lies", 2017)

"Any chart is a simplification of reality, and it reveals as much as it hides. Therefore, it’s always worth asking ourselves: What other patterns or trends may be hidden behind the data displayed on the chart?" (Alberto Cairo, "How Charts Lie", 2019)

"No chart can ever capture reality in all its richness. However, a chart can be made worse or better depending on its ability to strike a balance between oversimplifying that reality and obscuring it with too much detail."  (Alberto Cairo, "How Charts Lie", 2019)

🔭Data Science: Centrality (Just the Quotes)

"An average value is a single value within the range of the data that is used to represent all of the values in the series. Since an average is somewhere within the range of the data, it is sometimes called a measure of central value." (Frederick E Croxton & Dudley J Cowden,Practical Business Statistics", 1937)

"Some distributions [...] are symmetrical about their central value. Other distributions have marked asymmetry and are said to be skew. Skew distributions are divided into two types. If the 'tail' of the distribution reaches out into the larger values of the variate, the distribution is said to show positive skewness; if the tail extends towards the smaller values of the variate, the distribution is called negatively skew." (Michael J Moroney,Facts from Figures", 1951)

"Numerical data, which have been recorded at intervals of time, form what is generally described as a time series. [...] The purpose of analyzing time series is not always the determination of the trend by itself. Interest may be centered on the seasonal movement displayed by the series and, in such a case, the determination of the trend is merely a stage in the process of measuring and analyzing the seasonal variation. If a regular basic or under- lying seasonal movement can be clearly established, forecasting of future movements becomes rather less a matter of guesswork and more a matter of intelligent forecasting." (Alfred R Ilersic, "Statistics", 1959)

"Dispersion or spread is the degree of the scatter or variation of the variables about a central value." (Bertram C Brookes & W F L Dick,Introduction to Statistical Method", 1969)

"Equal variability is not always achieved in plots. For instance, if the theoretical distribution for a probability plot has a density that drops off gradually to zero in the tails" (as the normal density does), then the variability of the data in the tails of the probability plot is greater than in the center. Another example is provided by the histogram. Since the height of any one bar has a binomial distribution, the standard deviation of the height is approximately proportional to the square root of the expected height; hence, the variability of the longer bars is greater." (John M Chambers et al,Graphical Methods for Data Analysis", 1983)

"There are several reasons why symmetry is an important concept in data analysis. First, the most important single summary of a set of data is the location of the center, and when data meaning of 'center' is unambiguous. We can take center to mean any of the following things, since they all coincide exactly for symmetric data, and they are together for nearly symmetric data: (l) the center of symmetry. (2) the arithmetic average or center of gravity, (3) the median or 50%. Furthermore, if data a single point of highest concentration instead of several" (that is, they are unimodal), then we can add to the list (4) point of highest concentration. When data are far from symmetric, we may have trouble even agreeing on what we mean by center; in fact, the center may become an inappropriate summary for the data." (John M Chambers et al,Graphical Methods for Data Analysis", 1983)

"A connected graph is appropriate when the time series is smooth, so that perceiving individual values is not important. A vertical line graph is appropriate when it is important to see individual values, when we need to see short-term fluctuations, and when the time series has a large number of values; the use of vertical lines allows us to pack the series tightly along the horizontal axis. The vertical line graph, however, usually works best when the vertical lines emanate from a horizontal line through the center of the data and when there are no long-term trends in the data." (William S Cleveland,The Elements of Graphing Data", 1985)

"If the sample is not representative of the population because the sample is small or biased, not selected at random, or its constituents are not independent of one another, then the bootstrap will fail. […] For a given size sample, bootstrap estimates of percentiles in the tails will always be less accurate than estimates of more centrally located percentiles. Similarly, bootstrap interval estimates for the variance of a distribution will always be less accurate than estimates of central location such as the mean or median because the variance depends strongly upon extreme values in the population." (Phillip I Good & James W Hardin,Common Errors in Statistics" (and How to Avoid Them)", 2003)

"Central tendency is the formal expression for the notion of where data is centered, best understood by most readers as 'average'. There is no one way of measuring where data are centered, and different measures provide different insights." (Charles Livingston & Paul Voakes,Working with Numbers and Statistics: A handbook for journalists", 2005)

"Mean-averages can be highly misleading when the raw data do not form a symmetric pattern around a central value but instead are skewed towards one side [...], typically with a large group of standard cases but with a tail of a few either very high" (for example, income) or low" (for example, legs) values." (David Spiegelhalter,The Art of Statistics: Learning from Data", 2019)

"The elements of this cloud of uncertainty (the set of all possible errors) can be described in terms of probability. The center of the cloud is the number zero, and elements of the cloud that are close to zero are more probable than elements that are far away from that center. We can be more precise in this definition by defining the cloud of uncertainty in terms of a mathematical function, called the probability distribution." (David S Salsburg,Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Two clouds of uncertainty may have the same center, but one may be much more dispersed than the other. We need a way of looking at the scatter about the center. We need a measure of the scatter. One such measure is the variance. We take each of the possible values of error and calculate the squared difference between that value and the center of the distribution. The mean of those squared differences is the variance." (David S Salsburg,Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Betweenness Centrality makes the assumption that all communication between nodes happens along the shortest path and with the same frequency, which isn’t always the case in real life. Therefore, it doesn’t give us a perfect view of the most influential nodes in a graph, but rather a good representation." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)

"Centrality algorithms are used to understand the roles of particular nodes in a graph and their impact on that network. They’re useful because they identify the most important nodes and help us understand group dynamics such as credibility, accessibility, the speed at which things spread, and bridges between groups." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)

"Keep in mind that centrality measures represent the importance of a node in comparison to other nodes. Centrality is a ranking of the potential impact of nodes, not a measure of actual impact. For example, you might identify the two people with the highest cen‐ trality in a network, but perhaps policies or cultural norms are in play that actually shift influence to others. Quantifying actual impact is an active research area to develop additional influence metrics." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)

"Use Degree Centrality if you’re attempting to analyze influence by looking at the number of incoming and outgoing relationships, or find the 'popularity' of individual nodes. It works well when you’re concerned with immediate connectedness or near-term probabilities. However, Degree Centrality is also applied to global analysis when you want to evaluate the minimum degree, maximum degree, mean degree, and standard deviation across the entire graph." (Mark Needham & Amy E Hodler, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", 2019)

📉Graphical Representation: Rulings (Just the Quotes)

"A warning seems justifiable that the background of a chart should not be made any more prominent than actually necessary. Many charts have such heavy coordinate ruling and such relatively narrow lines for curves or other data that the real facts the chart is intended to portray do not stand out clearly from the background. No more coordinate lines should be used than are absolutely necessary to guide the eye of the reader and to permit an easy reading of the curves." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Co-ordinate ruling does not appear prominently on most original charts because the ruling is usually printed in some color of ink distinct from the curve itself. When, however, a chart is reproduced in a line engraving the co-ordinate lines come out the same color as the curve or other important data, and there may be too little contrast to assist the reader." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Graphic representation by means of charts depends upon the super-position of special lines or curves upon base lines drawn or ruled in a standard manner. For the economic construction of these charts as well as their correct use it is necessary that the standard rulings be correctly designed." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Admittedly a chart is primarily a picture, and for presentation purposes should be treated as such; but in most charts it is desirable to be able to read the approximate magnitudes by reference to the scales. Such reference is almost out of the question without some rulings to guide the eye. Second, the picture itself may be misleading without enough rulings to keep the eye 'honest'. Although sight is the most reliable of our senses for measuring" (and most other) purposes, the unaided eye is easily deceived; and there are numerous optical illusions to prove it. A third reason, not vital, but still of some importance, is that charts without rulings may appear weak and empty and may lack the structural unity desirable in any illustration." (Kenneth W Haemer, "Hold That Line. A Plea for the Preservation of Chart Scale Ruling", The American Statistician Vol. 1" (1) 1947)

"The grid with the vertical ruling carrying the logarithmic scale and the horizontal ruling carrying the arithmetic scale denoting time is the most common. The reverse may be used, and the horizontal ruling may carry the log scale. Charts of this type are frequently referred to as 'semilog charts'. [...] The full or double log scale (with the log grid carried on both horizontal and vertical rulings) is used mostly for statistical study and economic analysis and is not a good tool for popular presentation of data." (Mary E Spear, "Charting Statistics", 1952)

"In line charts the grid structure plays a controlling role in interpreting facts. The number of vertical rulings should be sufficient to indicate the frequency of the plottings, facilitate the reading of the time values on the horizontal scale. and indicate the interval or subdivision of time." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"The impression created by a chart depends to a great extent on the shape of the grid and the distribution of time and amount scales. When your individual figures are a part of a series make sure your own will harmonize with the other illustrations in spacing of grid rulings, lettering, intensity of lines, and planned to take the same reduction by following the general style of the presentation." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"The ratio chart not only correctly represents relative changes but also indicates absolute amounts at the same time. Because of its distinctive structure, it is referred to as a semilogarithmic chart. The vertical axis is ruled logarithmically and the horizontal axis arithmetically. The continued narrowing of the spacings of the scale divisions on the vertical axis is characteristic of logarithmic rulings; the equal intervals on the horizontal axis are indicative of arithmetic rulings." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"The scales used are important; contracting or expanding the vertical or horizontal scales will change the visual picture. The trend lines need enough grid lines to obviate difficulty in reading the results properly. One must be careful in the use of cross-hatching and shading, both of which can create illusions. Horizontal rulings tend to reduce the appearance. while vertical lines enlarge it. In summary, graphs must be reliable, and reliability depends not only on what is presented but also on how it is presented." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

📉Graphical Representation: Boxplots (Just the Quotes)

"Boxplots provide information at a glance about center (median), spread (interquartile range), symmetry, and outliers. With practice they are easy to read and are especially useful for quick comparisons of two or more distributions. Sometimes unexpected features such as outliers, skew, or differences in spread are made obvious by boxplots but might otherwise go unnoticed." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"A bar graph typically presents either averages or frequencies. It is relatively simple to present raw data" (in the form of dot plots or box plots). Such plots provide much more information. and they are closer to the original data. If the bar graph categories are linked in some way - for example, doses of treatments - then a line graph will be much more informative. Very complicated bar graphs containing adjacent bars are very difficult to grasp. If the bar graph represents frequencies. and the abscissa values can be ordered, then a line graph will be much more informative and will have substantially reduced chart junk." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Before calculating a confidence interval for a mean, first check that one of the situations just described holds. To determine whether the data are bell-shaped or skewed, and to check for outliers, plot the data using a histogram, dotplot, or stemplot. A boxplot can reveal outliers and will sometimes reveal skewness, but it cannot be used to determine the shape otherwise. The sample mean and median can also be compared to each other. Differences between the mean and the median usually occur if the data are skewed - that is, are much more spread out in one direction than in the other." (Jessica M Utts & Robert F Heckard, "Mind on Statistics", 2007)

"Symmetry and skewness can be judged, but boxplots are not entirely useful for judging shape. It is not possible to use a boxplot to judge whether or not a dataset is bell-shaped, nor is it possible to judge whether or not a dataset may be bimodal." (Jessica M Utts & Robert F Heckard, "Mind on Statistics", 2007)

"Sorting data is one of the most efficient actions to derive different views of data in order to see the variables from many angles. Sorting is usually not applied to the data itself, but to statistical objects of a plot. We might want to sort the bars in a barchart, the variables in a parallel boxplot or the categories in a boxplot y by x." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Need to consider outliers as they can affect statistics such as means, standard deviations, and correlations. They can either be explained, deleted, or accommodated (using either robust statistics or obtaining additional data to fill-in). Can be detected by methods such as box plots, scatterplots, histograms or frequency distributions." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"A boxplot is similar in spirit to an individual bar in a bar chart in that only a single spatial axis is used to visually encode data, but boxplots show five numbers through the use of a glyph rather than the single number encoded by the linear mark in a bar chart. A boxplot chart features multiple boxplots within a single shared frame to contrast different attribute distributions, just as bar charts show multiple bars along the second axis." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"Boxplots directly show the spread, namely, the degree of dispersion, with the extent of the box. They show the skew of the distribution compared with a normal distribution with the peak at the center by the asymmetry between the top and bottom sections of the box. Standard boxplots are designed to handle unimodal data, where there is only one value that occurs the most frequently. There are many variants of boxplots that augment the basic visual encoding with more information." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"A boxplot is a dotplot enhanced with a schematic that provides information about the center and spread of the data, including the median, quartiles, and so on. This is a very useful way of summarizing a variable's distribution. The dotplot can also be enhanced with a diamond-shaped schematic portraying the mean and standard deviation" (or the standard error of the mean)." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"Visual clutter is one of the most serious issues with bar charts. Using a bar to represent a simple data point is clearly overkill that results in no room for more data. At times, this may make us overlook less obvious things. The population pyramids offer a glaring example of this. But dot plots are not only about reducing clutter and avoiding overstimulation. Because we don’t compare heights, dot plots actually allow us to break the scale to improve resolution, and that’s a big plus over bar charts." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"[boxplots] allow you to assess variability both between and within the groups. [...] Each box shows the within-group variability, as measured by the interquartile range of the numerical variable (SAT score) for all cases in that category. The middle line within each box is the median of that category, and the differences between these medians give you a sense of the between-group variability. In this boxplot, the whiskers extend outside the box no further than 1.5 times the interquartile range. Points outside this interval are shown as individual dots." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"[…] the drawback of the box plot is that it tends to hide the values due to its design." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"There is no ‘correct’ way to display sets of numbers: each of the plots we have used has some advantages: strip-charts show individual points, box-and-whisker plots are convenient for rapid visual summaries, and histograms give a good feel for the underlying shape of the data distribution." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"Side-by-side box plots is a simpler approach that can give a crude understanding of the relationship between one quantitative variable and two or more qualitative variables. When we have many subgroups, side-by-side box-and-whisker plots can be very useful for comparing basic features of a distribution." (Deborah Nolan & Sara Stoudt, "Communicating with Data: The Art of Writing for Data Science", 2021)

"Side-by-side box plots offer a similar comparison of distributions across groups. The box plot offers a simpler approach that can give a crude understanding of a distribution. Likewise, violin plots sketch density curves along an axis for each group. The curve is flipped to create a symmetric 'violin' shape. The violin plot aims to bridge the gap between the density curve and box plot." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)

"The principle of symmetry states that objects that are symmetrical, or have a balanced appearance, tend to be perceived as a group or a pattern. Some data visualization graphs that can be used to explore this principle are the boxplot with boxes symmetrically placed around the median (Q2), the radar chart displaying multivariate data as a bidimensional chart with quantitative variables, and the mirrored bar chart with two sets of bars with mirrored values displayed." (Leandro N de Castro, "Exploratory Data Analysis: Descriptive Analysis, Visualization, and Dashboard Design", 2025)

📉Graphical Representation: Extremes (Just the Quotes)

"Missing data values pose a particularly sticky problem for symbols. For instance, if the ray corresponding to a missing value is simply left off of a star symbol, the result will be almost indistinguishable from a minimum (i.e., an extreme) value. It may be better either (i) to impute a value, perhaps a median for that variable, or a fitted value from some regression on other variables, (ii) to indicate that the value is missing, possibly with a dashed line, or (iii) not to draw the symbol for a particular observation if any value is missing." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"If the underlying pattern of the data has gentle curvature with no local maxima and minima, then locally linear fitting is usually sufficient. But if there are local maxima or minima, then locally quadratic fitting typically does a better job of following the pattern of the data and maintaining local smoothness." (William S Cleveland, "Visualizing Data", 1993)

"Variance and its square root, the standard deviation, summarize the amount of spread around the mean, or how much a variable varies. Outliers influence these statistics too, even more than they influence the mean. On the other hand. the variance and standard deviation have important mathematical advantages that make them (together with the mean) the foundation of classical statistics. If a distribution appears reasonably symmetrical, with no extreme outliers, then the mean and standard deviation or variance are the summaries most analysts would use." (Lawrence C Hamilton, "Data Analysis for Social Scientists: A first course in applied statistics", 1995)

"Clearly, the mean is greatly influenced by extreme values, but it can be appropriate for many situations where extreme values do not arise. To avoid misuse, it is essential to know which summary measure best reflects the data and to use it carefully. Understanding the situation is necessary for making the right choice. Know the subject!" (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)

"A feature shared by both the range and the interquartile range is that they are each calculated on the basis of just two values - the range uses the maximum and the minimum values, while the IQR uses the two quartiles. The standard deviation, on the other hand, has the distinction of using, directly, every value in the set as part of its calculation. In terms of representativeness, this is a great strength. But the chief drawback of the standard deviation is that, conceptually, it is harder to grasp than other more intuitive measures of spread." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Many scientists who work not just with noise but with probability make a common mistake: They assume that a bell curve is automatically Gauss's bell curve. Empirical tests with real data can often show that such an assumption is false. The result can be a noise model that grossly misrepresents the real noise pattern. It also favors a limited view of what counts as normal versus non-normal or abnormal behavior. This assumption is especially troubling when applied to human behavior. It can also lead one to dismiss extreme data as error when in fact the data is part of a pattern." (Bart Kosko, "Noise", 2006)

"Standard quantile graphs offer certain advantages over cumulative percent frequency graphs. Among these advantages are ease of construction, actual data points are shown as opposed to summaries of class intervals, no decisions are required as to what the best size class interval might be, the same curve functions as a less-than and greater-than curve, and the actual maximum and minimum values are shown on the graph." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996)

"[…] an outlier is an observation that lies an 'abnormal' distance from other values in a batch of data. There are two possible explanations for the occurrence of an outlier. One is that this happens to be a rare but valid data item that is either extremely large or extremely small. The other is that it is a mistake - maybe due to a measuring or recording error." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Plotting data is a useful first stage to any analysis and will show extreme observations together with any discernible patterns. In addition the relative sizes of categories are easier to see in a diagram" (bar chart or pie chart) than in a table. Graphs are useful as they can be assimilated quickly, and are particularly helpful when presenting information to an audience. Tables can be useful for displaying information about many variables at once, while graphs can be useful for showing multiple observations on groups or individuals. Although there are no hard and fast rules about when to use a graph and when to use a table, in the context of a report or a paper it is often best to use tables so that the reader can scrutinise the numbers directly." (Jenny Freeman et al, "How to Display Data", 2008)

📉Graphical Representation: Improvement (Just the Quotes)

"Graphical methodology provides powerful diagnostic tools for conveying properties of the fitted regression, for assessing the adequacy of the fit, and for suggesting improvements. There is seldom any prior guarantee that a hypothesized regression model will provide a good description of the mechanism that generated the data. Standard regression models carry with them many specific assumptions about the relationship between the response and explanatory variables and about the variation in the response that is not accounted for by the explanatory variables. In many applications of regression there is a substantial amount of prior knowledge that makes the assumptions plausible; in many other applications the assumptions are made as a starting point simply to get the analysis off the ground. But whatever the amount of prior knowledge, fitting regression equations is not complete until the assumptions have been examined." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"The illusion of randomness gradually disappears as the skill in chart reading improves." (John W. Murphy, "Technical Analysis of the Financial Markets", 1999)

"Always bear in mind that the purposes of any chart are (1) to help gather, organize or visualize the facts; (2) to aid in analyzing them; (3) to help in developing the better method and evaluating it; (4) to assist in convincing management of the improvement’s value." (Ben B Graham, "Detail Process Charting: Speaking the Language of Process", 2004)

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"The Sixth Principle for the analysis and display of data: 'Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content.' This suggests that the most effective way to improve a presentation is to get better content. It also suggests that design devices and gimmicks cannot salvage failed content." (Edward R Tufte, "Beautiful Evidence", 2006)

"Exploring data generates hypotheses about patterns in our data. The visualizations and tools of dynamic interactive graphics ease and improve the exploration, helping us to 'see what our data seem to say'." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"A performance dashboard is a practical tool to improve management effectiveness and efficiency, not just a pretty retrospective picture in an annual report." (Pearl Zhu, "Performance Master: Take a Holistic Approach to Unlock Digital Performance", 2017)

"Effective data scientists know that they are trying to convey accurate information in an easily understood way. We have never seen a pie chart that was an improvement over a simple table. Even worse, the creative addition of pictures, colors, shading, blots, and splotches may produce chartjunk that confuses the reader and strains the eyes." (Gary Smith & Jay Cordes, "The 9 Pitfalls of Data Science", 2019)

"Good design serves a more important function than simply pleasing you: It helps you access ideas. It improves your comprehension and makes the ideas more persuasive. Good design makes lesser charts good and good charts transcendent." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

📉Graphical Representation: Appropriateness (Just the Quotes)

"There is no doubt that graphical expression will soon replace all others whenever one has at hand a movement or change of state - in a word, any phenomenon. Born before science, language is often inappropriate to express exact measures or definite relations." (Étienne-Jules Marey, "La méthode graphique dans les sciences expérimentales et principalement en physiologie et en médecine", 1878)

"First, color has identity value. In other words, it serves to distinguish one thing from another. In many cases it does this much better and much quicker than black and white coding by different types of shading or lines. […] Second, color has suggestion value. […] Red is usually taken to mean a danger signal or an unfavorable condition. But since it is one of the most visible of colors it is excellent for adding emphasis, regardless of connotation. […] Green has no such unfavorable implication, and is usually appropriate for suggesting a green light" condition. […] Similarly, every color carries its own connotations; and although they seldom make a vital difference one way or the other, it seems logical to try to make them work for you rather than against you." (Kenneth W Haemer, "Color in Chart Presentation", The American Statistician Vol. 4 (2) , 1950)

"First, it is generally inadvisable to attempt to portray a series of more than four or five categories by means of pie charts. If, for example, there are six, eight, or more categories, it may be very confusing to differentiate the relative values portrayed, especially if several small sectors are of approximately the same size. Second, the pie chart may lose its effectiveness if an attempt is made to compare the component values of several circles, as might be found in a temporal or geographical series. In such case the one-hundred percent bar or column chart is more appropriate. Third, although the proportionate values portrayed in a pie chart are measured as distances along arcs about the circle, actually there is a tendency to estimate values in terms of areas of sectors or by the size of subtended angles at the center of the circle." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"The bar chart is one of the most useful, simple, adaptable, and popular techniques in graphic presentation. The simple bar chart. with its many variations, is particularly appropriate for comparing the magnitude, or size, of coordinate items or of parts of a total. The basis of comparison in the bar chart is linear or one-dimensional. The length of each bar or of its components is proportional to the quantity or amount of each category' represented. " (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"Simplicity, accuracy. appropriate size, proper proportion, correct emphasis, and skilled execution - these are the factors that produce the effective chart. To achieve simplicity your chart must be designed with a definite audience in mind, show only essential information. Technical terms should be absent as far as possible. And in case of doubt it is wiser to oversimplify than to make matters unduly complex. Be careful to avoid distortion or misrepresentation. Accuracy in graphics is more a matter of portraying a clear reliable picture than reiterating exact values. Selecting the right scales and employing authoritative titles and legends are as important as precision plotting. The right size of a chart depends on its probable use, its importance, and the amount of detail involved." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"The common bar chart is particularly appropriate for comparing magnitude or size of coordinate items or parts of a total. It is one of the most useful, simple, and adaptable techniques in graphic presentation. The basis of comparison in the bar chart is linear or one-dimensional. The length of each bar or of its components is proportional to the quantity or amount of each category represented." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"Charts and graphs are a method of organizing information for a unique purpose. The purpose may be to inform, to persuade, to obtain a clear understanding of certain facts, or to focus information and attention on a particular problem. The information contained in charts and graphs must, obviously, be relevant to the purpose. For decision-making purposes. information must be focused clearly on the issue or issues requiring attention. The need is not simply for 'information', but for structured information, clearly presented and narrowed to fit a distinctive decision-making context. An advantage of having a 'formula' or 'model' appropriate to a given situation is that the formula indicates what kind of information is needed to obtain a solution or answer to a specific problem." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Data should not be forced into an uncomfortable or improper mold. For example, data that is appropriate for line graphs is not usually appropriate for circle charts and in any case not without some arithmetic transformation. Only graphs that are designed to fit the data can be used profitably." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Understanding is accomplished through: (a) the use of relative size of the shapes used in the graphic; (b) the positioning of the graphic-line forms; (c) shading; (d) the use of scales of measurement; and (e) the use of words to label the forms in the graphic. In addition. in order for a person to attach meaning to a graphic it must also be simple, clear, and appropriate." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"There are several reasons why symmetry is an important concept in data analysis. First, the most important single summary of a set of data is the location of the center, and when data meaning of 'center' is unambiguous. We can take center to mean any of the following things, since they all coincide exactly for symmetric data, and they are together for nearly symmetric data: (l) the center of symmetry. (2) the arithmetic average or center of gravity, (3) the median or 50%. Furthermore, if data a single point of highest concentration instead of several (that is, they are unimodal), then we can add to the list (4) point of highest concentration. When data are far from symmetric, we may have trouble even agreeing on what we mean by center; in fact, the center may become an inappropriate summary for the data." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"In order to be easily understood, a display of information must have a logical structure which is appropriate for the user's knowledge and needs, and this structure must be clearly represented visually. In order to indicate structure, it is necessary to be able to eemphasiz, divide and relate items of information. Visual emphasis can be used to indicate a hierarchical relationship between items of information, as in the case of systems of headings and subheadings for example. Visual separation of items can be used to indicate that they are different in kind or are unrelated functionally, and similarly a visual relationship between items will imply that they are of a similar kind or bear some functional relation to one another. This kind of visual 'coding' helps the reader to appreciate the extent and nature of the relationship between items of information, and to adopt an appropriate scanning strategy." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"The effective communication of information in visual form, whether it be text, tables, graphs, charts or diagrams, requires an understanding of those factors which determine the 'legibility', 'readability' and 'comprehensibility', of the information being presented. By legibility we mean: can the data be clearly seen and easily read? By readability we mean: is the information set out in a logical way so that its structure is clear and it can be easily scanned? By comprehensibility we mean: does the data make sense to the audience for whom it is intended? Is the presentation appropriate for their previous knowledge, their present information needs and their information processing capacities?" (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"[…] the partial scale break is a weak indicator that the reader can fail to appreciate fully; visually the graph is still a single panel that invites the viewer to see, inappropriately, patterns between the two scales. […] The partial scale break also invites authors to connect points across the break, a poor practice indeed; […]" (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38 (4) 1984) 

"A connected graph is appropriate when the time series is smooth, so that perceiving individual values is not important. A vertical line graph is appropriate when it is important to see individual values, when we need to see short-term fluctuations, and when the time series has a large number of values; the use of vertical lines allows us to pack the series tightly along the horizontal axis. The vertical line graph, however, usually works best when the vertical lines emanate from a horizontal line through the center of the data and when there are no long-term trends in the data." (William S Cleveland, "The Elements of Graphing Data", 1985)

"Visual displays rich with data are not only an appropriate and proper complement to human capabilities, but also such designs are frequently optimal. If the visual task is contrast, comparison, and choice - as so often it is - then the more relevant information within eyespan, the better. Vacant, low-density displays, the dreaded posterization of data spread over pages and pages, require viewers to rely on visual memory - a weak skill - to make a contrast, a comparison, a choice." (Edward R Tufte, "Envisioning Information", 1990)

"We analyze numbers in order to know when a change has occurred in our processes or systems. We want to know about such changes in a timely manner so that we can respond appropriately. While this sounds rather straightforward, there is a complication - the numbers can change even when our process does not. So, in our analysis of numbers, we need to have a way to distinguish those changes in the numbers that represent changes in our process from those that are essentially noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Every statistical analysis is an interpretation of the data, and missingness affects the interpretation. The challenge is that when the reasons for the missingness cannot be determined there is basically no way to make appropriate statistical adjustments. Sensitivity analyses are designed to model and explore a reasonable range of explanations in order to assess the robustness of the results." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"The content and context of the numerical data determines the most appropriate mode of presentation. A few numbers can be listed, many numbers require a table. Relationships among numbers can be displayed by statistics. However, statistics, of necessity, are summary quantities so they cannot fully display the relationships, so a graph can be used to demonstrate them visually. The attractiveness of the form of the presentation is determined by word layout, data structure, and design." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"The plot tells us the data are granular in the data source, something we could not ascertain with the histogram. There is an important lesson here. Statistics texts and statistical packages that recommend the histogram as the graphical starting point for a data analysis are giving bad advice. The same goes for kernel density estimates. These are appropriate second stages for graphical data analysis. The best starting point for getting a sense of the distribution of a variable is a tally, stem-and-leaf, or a dot plot. A dot plot is a special case of a tally" (perhaps best thought of as a delta-neighborhood tally). Once we see that the data are not granular, we may move on to a histogram or kernel density, which smooths the data more than a dot plot." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"[...] the First Principle for the analysis and presentation data: 'Show comparisons, contrasts, differences'. The fundamental analytical act in statistical reasoning is to answer the question "Compared with what?". Whether we are evaluating changes over space or time, searching big data bases, adjusting and controlling for variables, designing experiments , specifying multiple regressions, or doing just about any kind of evidence-based reasoning, the essential point is to make intelligent and appropriate comparisons. Thus visual displays, if they are to assist thinking, should show comparisons." (Edward R Tufte, "Beautiful Evidence", 2006)

"A histogram consists of the outline of bars of equal width and appropriate length next to each other. By connecting the frequency values at the position of the nominal values" (the midpoints of the intervals) with straight lines, a frequency polygon is obtained. Attaching classes with frequency zero at either end makes the area" (the integral) under the frequency polygon equal to that under the histogram." (Manfred Drosg, "Dealing with Uncertainties: A Guide to Error Analysis", 2007)

"Numerical precision should be consistent throughout and summary statistics such as means and standard deviations should not have more than one extra decimal place" (or significant digit) compared to the raw data. Spurious precision should be avoided although when certain measures are to be used for further calculations or when presenting the results of analyses, greater precision may sometimes be appropriate." (Jenny Freeman et al, "How to Display Data", 2008)

"There are two main reasons for using graphic displays of datasets: either to present or to explore data. Presenting data involves deciding what information you want to convey and drawing a display appropriate for the content and for the intended audience. [...] Exploring data is a much more individual matter, using graphics to find information and to generate ideas. Many displays may be drawn. They can be changed at will or discarded and new versions prepared, so generally no one plot is especially important, and they all have a short life span." (Antony Unwin, "Good Graphics?" [in "Handbook of Data Visualization"], 2008)

"When displaying information visually, there are three questions one will find useful to ask as a starting point. Firstly and most importantly, it is vital to have a clear idea about what is to be displayed; for example, is it important to demonstrate that two sets of data have different distributions or that they have different mean values? Having decided what the main message is, the next step is to examine the methods available and to select an appropriate one. Finally, once the chart or table has been constructed, it is worth reflecting upon whether what has been produced truly reflects the intended message. If not, then refine the display until satisfied; for example if a chart has been used would a table have been better or vice versa?" (Jenny Freeman et al, "How to Display Data", 2008)

"The problem of overplotting can be as severe that (smaller) groups can disappear completely, which will not only lead to quantitatively biased inferences, but even to qualitatively inappropriate conclusions." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009) 

"In order to be effective a descriptive statistic has to make sense - it has to distill some essential characteristic of the data into a value that is both appropriate and understandable. […] the justification for computing any given statistic must come from the nature of the data themselves - it cannot come from the arithmetic, nor can it come from the statistic. If the data are a meaningless collection of values, then the summary statistics will also be meaningless - no arithmetic operation can magically create meaning out of nonsense. Therefore, the meaning of any statistic has to come from the context for the data, while the appropriateness of any statistic will depend upon the use we intend to make of that statistic." (Donald J Wheeler, "Myths About Data Analysis", International Lean & Six Sigma Conference, 2012) 

"Visualization ethics relates to the potential deception that can be created, intentionally or otherwise, from an ineffective and inappropriate representation of data. Sometimes it can be through a simple lack of understanding of visual perception." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"There are two kinds of mistakes that an inappropriate inductive bias can lead to: underfitting and overfitting. Underfitting occurs when the prediction model selected by the algorithm is too simplistic to represent the underlying relationship in the dataset between the descriptive features and the target feature. Overfitting, by contrast, occurs when the prediction model selected by the algorithm is so complex that the model fits to the dataset too closely and becomes sensitive to noise in the data."(John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"When using indexes in a data set, using an average aggregation is appropriate as long as you only use it at the individual region, month, and visitor type level (i.e., the lowest granularity of the data). You cannot use an average of the average to represent the total."  (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"The second rule of communication is to know what you want to achieve. Hopefully the aim is to encourage open debate, and informed decision-making. But there seems no harm in repeating yet again that numbers do not speak for themselves; the context, language and graphic design all contribute to the way the communication is received. We have to acknowledge we are telling a story, and it is inevitable that people will make comparisons and judgements, no matter how much we only want to inform and not persuade. All we can do is try to pre-empt inappropriate gut reactions by design or warning." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"For numbers to be transparent, they must be placed in an appropriate context. Numbers must presented in a way that allows for fair comparisons." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"To tell an honest story, it is not enough for numbers to be correct. They need to be placed in an appropriate context so that a reader or listener can properly interpret them." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Raw data without appropriate visualization is like dumped construction raw materials at a building construction site. The finished house is the actual visuals created from those data like raw materials." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"[...] to support a conversation, charts need to provide cohesive and relevant responses to a user's intent. Sometimes the interface needs to respond by changing the visual encoding of existing charts, while in other cases, it is necessary to create a new chart to support the analytical conversation. In addition to appropriate visualization responses, it is critical to help the user understand how the system has interpreted their intent by producing appropriate feedback and allowing them to clarify if necessary." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"If an organization had a single overall data quality key performance indicator (KPI), then it might be appropriate to put a greater weighting on those rules which would impact regulatory compliance. A lack of regulatory compliance is a risk to the very existence of organizations like these, and therefore, a greater weighting might be needed." (Robert Hawker, "Practical Data Quality", 2023)