📉Graphical Representation: Adaptation (Just the Quotes)

"It is not possible to lay down any hard and fast rules for determining what chart is the best for any given problem. Ordinarily that one is the best which will produce the quickest and clearest results. but unfortunately it is not always possible to construct the clearest one in the least time. Experience is the best guide. Generally speaking, a rectilinear chart is best adapted for equations of the first degree, logarithmic for those other than the first degree and not containing over two variables, and alignment charts where there are three or more variables. However, nearly every person becomes more or less familiar with one type of chart and prefers to adhere to the use of that type because he does not care to take the time and trouble to find out how to use the others. It is best to know what the possibilities of all types are and to be governed accordingly when selecting one or the other for presenting or working out certain data." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"A chart without a border line has several advantages. It is not limited to a designated area. The irregular white space surrounding it makes it more adaptable to any page size. It may be more readily placed either horizontally or vertically on the page, so long as the reduction in the size of the chart does not destroy legibility of lettering." (Mary E Spear, "Charting Statistics", 1952)

"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)

"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)

"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)

"It is almost impossible to define 'time-sequence chart' in a clear and unambiguous manner because of the many forms and adaptations open to this type of chart. However. it might be said that, in essence, time-sequence chart portrays a chain of activities through time, indicates the type of activity in each link of the chain, shows clearly the position of the link in the total sequence chain, and indicates the duration of each activity. The time sequence chart may also contain verbal elements explaining when to begin an activity, how long to continue the activity, and a description of the activity. The chart may also indicate when to blend a given activity with another and the point at which a given activity is completed. The basic time-sequence chart may also be accompanied by verbal explanations and by secondary or contributory charts." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Working with binned data directly addresses large data set issues of computation and plotting speed. Almost everything that can bc done with the original data can be done faster with binned data. Further, working with binned data allows image processing algorithms to be adapted and applied to bin cells. Thus tools can bc brought to bare that are not traditionally associated with exploratory data analysis." (Daniel B Carr, "Looking at Large Data Sets Using Binned Data Plots", [in "Computing and Graphics in Statistics"] 1991)

"The real value of dashboard products lies in their ability to replace hunt‐and‐peck data‐gathering techniques with a tireless, adaptable, information‐flow mechanism. Dashboards transform data repositories into consumable information." (Gregory L Hovis, "Stop Searching for Information Monitor it with Dashboard Technology," DM Direct, 2002)

"Why does representing information in terms of natural frequencies rather than probabilities or percentages foster insight? For two reasons. First, computational simplicity: The representation does part of the computation. And second, evolutionary and developmental primacy: Our minds are adapted to natural frequencies." (Gerd Gigerenzer, "Calculated Risks: How to know when numbers deceive you", 2002)

"Put everything together - from understanding data, to exploration, clarity, and adapting to an audience - and you get a general process for how to make data graphics." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

📉Graphical Representation: Problem Solving (Just the Quotes)

"[...] we can not readily break up a complicated problem into successive steps which can be taken independently. We have, in fact, to solve the problem first, by determining what are the actual mutual relations of the classes involved, and then to draw the circles to represent this final result; we cannot work step-by-step towards the conclusion by aid of our figures." (John Venn, "On the Diagrammatic and Mechanical Representation of Propositions and Reasonings", 1880)

"One of the greatest values of the graphic chart is its use in the analysis of a problem. Ordinarily, the chart brings up many questions which require careful consideration and further research before a satisfactory conclusion can be reached. A properly drawn chart gives a cross-section picture of the situation. While charts may bring out. hidden facts in tables or masses of data, they cannot take the place of careful, analysis. In fact, charts may be dangerous devices when in the hands of those unwilling to base their interpretations upon careful study. This, however, does not detract from their value when they are properly used as aids in solving statistical problems." (John R Riggleman & Ira N Frisbee, "Business Statistics", 1938)

"90 percent of all problems can be solved by using the techniques of data stratification, histograms, and control charts. Among the causes of nonconformance, only one-fifth or less are attributable to the workers." (Kaoru Ishikawa, The Quality Management Journal Vol. 1, 1993)

"Visual thinking can begin with the three basic shapes we all learned to draw before kindergarten: the triangle, the circle, and the square. The triangle encourages you to rank parts of a problem by priority. When drawn into a triangle, these parts are less likely to get out of order and take on more importance than they should. While the triangle ranks, the circle encloses and can be used to include and/or exclude. Some problems have to be enclosed to be managed. Finally, the square serves as a versatile problem-solving tool. By assigning it attributes along its sides or corners, we can suddenly give a vague issue a specific place to live and to move about." (Terry Richey, "The Marketer's Visual Tool Kit", 1994)

"Good numeric representation is a key to effective thinking that is not limited to understanding risks. Natural languages show the traces of various attempts at finding a proper representation of numbers. [...] The key role of representation in thinking is often downplayed because of an ideal of rationality that dictates that whenever two statements are mathematically or logically the same, representing them in different forms should not matter. Evidence that it does matter is regarded as a sign of human irrationality. This view ignores the fact that finding a good representation is an indispensable part of problem solving and that playing with different representations is a tool of creative thinking." (Gerd Gigerenzer, "Calculated Risks: How to know when numbers deceive you", 2002)

"Estimating the missing values in a dataset solves one problem - imputing reasonable values that have well-defined statistical properties. It fails to solve another, however - drawing inferences about parameters in a model fit to the estimated data. Treating imputed values as if they were known (like the rest of the observed data) causes confidence intervals to be too narrow and tends to bias other estimates that depend on the variability of the imputed values (such as correlations)." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Design is a goal-oriented, problem-solving activity that typically takes many iterations - teams rarely come up with the 'optimal' design the first time around. [...] With each model, improvements were made to the original design such that the minivans of today are much improved compared to the initial product. The key activity in the design process is the development and testing of a descriptive model of the finished product before the product is finally manufactured or constructed." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Complex problems require the perspectives of different disciplines to solve for them. In a cross-disciplinary team these perspectives also bring their own practices, tools, languages, and data sets. Overlaps and differences between disciplines are negotiated as teams attempt to integrate the knowledge and information each discipline brings to bear on the problem. A challenge is making sense out of the disparate sets of information describing the various aspects of a problem." (Joanne Mendel & Jan Yeager, "Knowledge Visualization in Design Practice: Exploring the power of knowledge visualization in problem solving", Parsons Journal of Information Mapping Vol. II(3), 2010)

"Graphics is the visual means of resolving logical problems." (Jacques Bertin, "Graphics and Graphic Information Processing", 2011)

"We are all becoming more comfortable with data. Data visualization is no longer just something we have to do at work. Increasingly, we want to do it as consumers and as citizens. Put simply, visualizing helps us understand what’s going on in our lives - and how to solve problems." (Phil Simon, "The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions", 2014)

"Creating effective visualizations is hard. Not because a dataset requires an exotic and bespoke visual representation - for many problems, standard statistical charts will suffice. And not because creating a visualization requires coding expertise in an unfamiliar programming language [...]. Rather, creating effective visualizations is difficult because the problems that are best addressed by visualization are often complex and ill-formed. The task of figuring out what attributes of a dataset are important is often conflated with figuring out what type of visualization to use. Picking a chart type to represent specific attributes in a dataset is comparatively easy. Deciding on which data attributes will help answer a question, however, is a complex, poorly defined, and user-driven process that can require several rounds of visualization and exploration to resolve." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

📉Graphical Representation: Understanding (Just the Quotes)

"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)

"If two or more data paths ate to appear on the graph. it is essential that these lines be labeled clearly, or at least a reference should be provided for the reader to make the necessary identifications. While clarity seems to be a most obvious goal, graphs with inadequate or confusing labeling do appear in publications, The user should not find identification of data paths troublesome or subject to misunderstanding. The designer normally should place no more than three data paths on the graph to prevent confusion - particularly if the data paths intersect at one or more points on the Cartesian plane." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Learning to make graphs involves two things: (l) the techniques of plotting statistics, which might be called the artist's job; and (2) understanding the statistics. When you know how to work out graphs, all kinds of statistics will probably become more interesting to you." (Dyno Lowenstein, "Graphs", 1976)

"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)

"We would wish ‘numerate’ to imply the possession of two attributes. The first of these is an ‘at-homeness’ with numbers and an ability to make use of mathematical skills which enable an individual to cope with the practical mathematical demands of his everyday life. The second is ability to have some appreciation and understanding of information which is presented in mathematical terms, for instance in graphs, charts or tables or by reference to percentage increase or decrease." (Cockcroft Committee, "Mathematics Counts: A Report into the Teaching of Mathematics in Schools", 1982)

"Graphs can present internal accounting data effectively. Because one of the main functions of the accountant is to communicate accounting information to users. accountants should use graphs, at least to the extent that they clarify the presentation of accounting data. present the data fairly, and enhance management's ability to make a more informed decision. It has been argued that the human brain can absorb and understand images more easily than words and numbers, and, therefore, graphs may be better communicative devices than written reports or tabular statements." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Understandability implies that the graph will mean something to the audience. If the presentation has little meaning to the audience, it has little value. Understandability is the difference between data and information. Data are facts. Information is facts that mean something and make a difference to whoever receives them. Graphic presentation enhances understanding in a number of ways. Many people find that the visual comparison and contrast of information permit relationships to be grasped more easily. Relationships that had been obscure become clear and provide new insights." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"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)

"In part, graphing data needs to be iterative because we often do not know what to expect of the data; a graph can help discover unknown aspects of the data, and once the unknown is known, we frequently find ourselves formulating a new question about the data. Even when we understand the data and are graphing them for presentation, a graph will look different from what we had expected; our mind's eye frequently does not do a good job of predicting what our actual eyes will see." (William S Cleveland, "The Elements of Graphing Data", 1985)

"A chart is a bridge between you and your readers. It reveals your skills at comprehending the source information, at mastering presentation methods and at producing the design. Its success depends a great deal on your readers ' understanding of what you are saying, and how you are saying it. Consider how they will use your chart. Will they want to find out from it more information about the subject? Will they just want a quick impression of the data? Or will they use it as a source for their own analysis? Charts rely upon a visual language which both you and your readers must understand." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Charts and diagrams are the visual presentation of information. Since text and tables of information require close study to obtain the more general impressions of the subject, charts can be used to present readily understandable, easily digestible and, above all, memorable solutions." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Charts offer opportunities to distort information, to misinform. An old adage can be extended to read: 'There are lies, damned lies, statistics and charts'. Our visual impressions are often more memorable than our understanding of the facts they describe. [...] Never let your design enthusiasms overrule your judgement of the truth." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Confusion and clutter are failures of design, not attributes of information. And so the point is to find design strategies that reveal detail and complexity - rather than to fault the data for an excess of complication. Or, worse, to fault viewers for a lack of understanding. Among the most powerful devices for reducing noise and enriching the content of displays is the technique of layering and separation, visually stratifying various aspects of the data." (Edward R Tufte, "Envisioning Information", 1990)

"When analyzing data it is many times advantageous to generate a variety of graphs using the same data. This is true whether there is little or lots of data. Reasons for this are: (1) Frequently, all aspects of a group of data can not be displayed on a single graph. (2) Multiple graphs generally result in a more in-depth understanding of the information. (3) Different aspects of the same data often become apparent. (4) Some types of graphs cause certain features of the data to stand out better (5) Some people relate better to one type of graph than another." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"Most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations. No matter how great the technology, a dashboard's success as a medium of communication is a product of design, a result of a display that speaks clearly and immediately. Dashboards can tap into the tremendous power of visual perception to communicate, but only if those who implement them understand visual perception and apply that understanding through design principles and practices that are aligned with the way people see and think." (Stephen Few, "Information Dashboard Design", 2006)

"Specific numbers, visual descriptions of objects or events and identifiable locations don’t always jump out, and a graphic may not always present itself right away. A good graphics reporter will often discover graphics potential in less obvious ways. Is the explanation in a story getting bogged down and hard to follow? If so, can the information be organized differently? Perhaps in a more graphic manner? Is there information that hat can be conveyed conceptually to put a thought or idea into a more visual perspective? Visual metaphors (or 'data metaphors' in the case of mathematical or quantifiable information) often make it easier for people to digest information." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"The purpose of an evidence presentation is to assist thinking. Thus presentations should be constructed so as to assist with the fundamental intellectual tasks in reasoning about evidence: describing the data, making multivariate comparisons, understanding causality, integrating a diversity of evidence, and documenting the analysis. Thus the Grand Principle of analytical design: 'The principles of analytical design are derived from the principles of analytical thinking.' Cognitive tasks are turned into principles of evidence presentation and design." (Edward R Tufte, "Beautiful Evidence", 2006)

"A viewer’s eye must be guided to 'read' the elements in a logical order. The design of an exploratory graphic needs to allow for the additional component of discovery - guiding the viewer to first understand the overall concept and then engage her to further explore the supporting information." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Context (information that lends to better understanding the who, what, when, where, and why of your data) can make the data clearer for readers and point them in the right direction. At the least, it can remind you what a graph is about when you come back to it a few months later. […] Context helps readers relate to and understand the data in a visualization better. It provides a sense of scale and strengthens the connection between abstract geometry and colors to the real world." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"A good chart can tell a story about the data, helping you understand relationships among data so you can make better decisions. The wrong chart can make a royal mess out of even the best data set." (John H Johnson & Mike Gluck, "Everydata: The misinformation hidden in the little data you consume every day", 2016)

"A scatterplot reveals the strength and shape of the relationship between a pair of variables. A scatterplot represents the two variables by axes drawn at right angles to each other, showing the observations as a cloud of points, each point located according to its values on the two variables. Various lines can be added to the plot to help guide our search for understanding." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"One of the main problems with the visual approach to statistical data analysis is that it is too easy to generate too many plots: We can easily become totally overwhelmed by the shear number and variety of graphics that we can generate. In a sense, we have been too successful in our goal of making it easy for the user: Many, many plots can be generated, so many that it becomes impossible to understand our data." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"As a first principle, any visualization should convey its information quickly and easily, and with minimal scope for misunderstanding. Unnecessary visual clutter makes more work for the reader’s brain to do, slows down the understanding (at which point they may give up) and may even allow some incorrect interpretations to creep in." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Well-designed data graphics provide readers with deeper and more nuanced perspectives, while promoting the use of quantitative information in understanding the world and making decisions." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Communicating data through functionally aesthetic charts is not only about perception and precision but also understanding." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Communication requires the ability to expand or contract a message based on norms within a given culture or language. Expansion provides more detail, sometimes adding in information that is culturally relevant or needed for the person to understand. Contraction preserves the same intent but discards information that isn't needed by that person. Some concepts in certain situations require greater detail than others." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Semantic use of color supports the understanding of what the visualization is conveying. When color is used for a specific paradigm, those using the visualization can follow that paradigm. One paradigm might be using a specific color to highlight selections on an otherwise monochrome visualization. In others, color may be categorical but match associations with the time of day [...]. Color can also help direct attention to differences in the data." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Understanding language goes hand in hand with the ability to integrate complex contextual information into an effective visualization and being able to converse with the data interactively, a term we call analytical conversation. It also helps us think about ways to create artifacts that support and manage how we converse with machines as we see and understand data."(Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Understanding the context and the domain of the data is important to help disambiguate concepts. While reasonable defaults can be used to create a visualization, there should be no dead ends. Provide affordances for a user to understand, repair, and refine." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"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)

"We see first what stands out. Our eyes go right to change and difference - peaks, valleys, intersections, dominant colors, outliers. Many successful charts - often the ones that please us the most and are shared and talked about - exploit this inclination by showing a single salient point so clearly that we feel we understand the chart’s meaning without even trying." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

📉Graphical Representation: Meaning (Just the Quotes)

"It is desirable in all chart work to have certain conventions by which colors would be understood to have certain definite meanings. Thus, following railroad practice, red could generally be used in chart work to indicate dangerous or unfavorable conditions, and green to indicate commended features or favorable conditions. Where neither commendation nor adverse criticism is intended, colors such as blue, yellow, brown, etc., could be used." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"Graphic forms help us to perform and influence two critical functions of the mind: the gathering of information and the processing of that information. Graphs and charts are ways to increase the effectiveness and the efficiency of transmitting information in a way that enhances the reader's ability to process that information. Graphics are tools to help give meaning to information because they go beyond the provision of information and show relationships, trends, and comparisons. They help to distinguish which numbers and which ideas are more important than others in a presentation." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"The more complex the shape of any object. the more difficult it is to perceive it. The nature of thought based on the visual apprehension of objective forms suggests, therefore, the necessity to keep all graphics as simple as possible. Otherwise, their meaning will be lost or ambiguous, and the ability to convey the intended information and to persuade will be inhibited." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"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 two kinds of misrepresentation. In one. the numerical data do not agree with the data in the graph, or certain relevant data are omitted. This kind of misleading presentation. while perhaps hard to determine, clearly is wrong and can be avoided. In the second kind of misrepresentation, the meaning of the data is different to the preparer and to the user." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"Understandability implies that the graph will mean something to the audience. If the presentation has little meaning to the audience, it has little value. Understandability is the difference between data and information. Data are facts. Information is facts that mean something and make a difference to whoever receives them. Graphic presentation enhances understanding in a number of ways. Many people find that the visual comparison and contrast of information permit relationships to be grasped more easily. Relationships that had been obscure become clear and provide new insights." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"There is a technical difference between a bar chart and a histogram in that the number represented is proportional to the length of bar in the former and the area in the latter. This matters if non-uniform binning is used. Bar charts can be used for qualitative or quantitative data, whereas histograms can only be used for quantitative data, as no meaning can be attached to the width of the bins if the data are qualitative." (Roger J Barlow, "Statistics: A guide to the use of statistical methods in the physical sciences", 1989)

"The more clues to meaning that are supplied elsewhere, the less the need for cluttersome scales." (Eric Meyer, "Designing Infographics", 1997) 

"[...] the form of a technological object must depend on the tasks it should help with. This is one of the most important principles to remember when dealing with infographics and visualizations: The form should be constrained by the functions of your presentation. There may be more than one form a data set can adopt so that readers can perform operations with it and extract meanings, but the data cannot adopt any form. Choosing visual shapes to encode information should not be based on aesthetics and personal tastes alone." (Alberto Cairo, "The Functional Art", 2011)

"To keep accuracy and efficiency of your diagrams appealing to a potential audience, explicitly describe the encoding principles we used. Titles, labels, and legends are the most common ways to define the meaning of the diagram and its elements." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"Essentially, magnitude is the size of the effect. It’s a way to determine if the results are meaningful. Without magnitude, it’s hard to get a sense of how much something matters. […] the magnitude of an effect can change, depending on the relationship." (John H Johnson & Mike Gluck, "Everydata: The misinformation hidden in the little data you consume every day", 2016)

"Are your insights based on data that is accurate and reliable? Trustworthy data is correct or valid, free from significant defects and gaps. The trustworthiness of your data begins with the proper collection, processing, and maintenance of the data at its source. However, the reliability of your numbers can also be influenced by how they are handled during the analysis process. Clean data can inadvertently lose its integrity and true meaning depending on how it is analyzed and interpreted." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Before you can even consider creating a data story, you must have a meaningful insight to share. One of the essential attributes of a data story is a central or main insight. Without a main point, your data story will lack purpose, direction, and cohesion. A central insight is the unifying theme (telos appeal) that ties your various findings together and guides your audience to a focal point or climax for your data story. However, when you have an increasing amount of data at your disposal, insights can be elusive. The noise from irrelevant and peripheral data can interfere with your ability to pinpoint the important signals hidden within its core." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. Ample context and commentary are often needed to fully appreciate an analysis finding. The narrative element adds structure to the data and helps to guide the audience through the meaning of what’s being shared." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

📉Graphical Representation: Coordinates (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)

"A system may be specified in either of two ways. In the first, which we shall call a state description, sets of abstract inputs, outputs and states are given, together with the action of the inputs on the states and the assignments of outputs to states. In the second, which we shall call a coordinate description, certain input, output and state variables are given, together with a system of dynamical equations describing the relations among the variables as functions of time. Modern mathematical system theory is formulated in terms of state descriptions, whereas the classical formulation is typically a coordinate description, for example a system of differential equations." (E S Bainbridge, "The Fundamental Duality of System Theory", 1975)

"A coordinate is a number or value used to locate a point with respect to a reference point, line, or plane. Generally the reference is zero. […] The major function of coordinates is to provide a method for encoding information on charts, graphs, and maps in such a way that viewers can accurately decode the information after the graph or map has been generated."  (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"Coordinates are sets that locate points in space. These sets are usually numbers grouped in tuples, one tuple for each point. Because spaces can be defined as sets of geometric objects plus axioms defining their behavior, coordinates can be thought of more generally as schemes for mapping elements of sets to geometric objects." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"No other statistical graphic can hold so much information at a time than the parallel coordinate plot. Thus this plot is ideal to get an initial overview of a dataset, or at the very least a large subgroup of the variables." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"One big advantage of parallel coordinate plots over scatterplot matrices. (i.e., the matrix of scatterplots of all variable pairs) is that parallel coordinate plots need less space to plot the same amount of data. On the other hand, parallel coordinate plots with p variables show only p - 1 adjacencies. However, adjacent variables reveal most of the information in a parallel coordinate plot. Reordering variables in a parallel coordinate plot is therefore essential." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009) 

"Parallel coordinate plots are often overrated concerning their ability to depict multivariate features. Scatterplots are clearly superior in investigating the relationship between two continuous variables and multivariate outliers do not necessarily stick out in a parallel coordinate plot. Nonetheless, parallel coordinate plots can help to find and understand features such as groups/clusters, outliers and multivariate structures in their multivariate context. The key feature is the ability to select and highlight individual cases or groups in the data, and compare them to other groups or the rest of the data." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"The idiom of parallel coordinates is an approach for visualizing many quantitative attributes at once using spatial position. As the name suggests, the axes are placed parallel to each other, rather than perpendicularly at right angles. While an item is shown with a dot in a scatterplot, with parallel coordinates a single item is represented by a jagged line that zigzags through the parallel axes, crossing each axis exactly once at the location of the item’s value for the associated attribute. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

🪙Business Intelligence: Storytelling (Just the Quotes)

"Storytelling reveals meaning without committing the error of defining it." (Hannah Arendt, "Men in Dark Times", 1968)

"Scientific practice may be considered a kind of storytelling practice [...]" (Donna Haraway, "Primate Visions", 1989)

"Storytelling is the art of unfolding knowledge in a way that makes each piece contribute to a larger truth." (Philip Gerard, "Writing a Book That Makes a Difference", 2000)

"The human mind is a wanton storyteller and even more, a profligate seeker after pattern. We see faces in clouds and tortillas, fortunes in tea leaves and planetary movements. It is quite difficult to prove a real pattern as distinct from a superficial illusion." (Richard Dawkins, "A Devil's Chaplain", 2003)

"The world of a story is not merely the sum of all the words we put on a page, or on many pages. When we talk about entering the world of a story as a reader we refer to things we picture, or imagine, and responses we form - to characters, events - all of which are prompted by, but not entirely encompassed by, the words on the page." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"We have, as human beings, a storytelling problem. We're a bit too quick to come up with explanations for things we don't really have an explanation for." (Malcolm Gladwell, "Blink: The Power of Thinking Without Thinking", 2005)

"There is an extraordinary power in storytelling that stirs the imagination and makes an indelible impression on the mind." (Brennan Manning, "The Ragamuffin Gospel: Good News for the Bedraggled, Beat-Up, and Burnt Out", 2008)

"Mostly we rely on stories to put our ideas into context and give them meaning. It should be no surprise, then, that the human capacity for storytelling plays an important role in the intrinsically human-centered approach to problem solving, design thinking." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"The purpose of a storyteller is not to tell you how to think, but to give you questions to think upon." (Brandon Sanderson, "The Way of Kings", 2010)

"Visualizations act as a campfire around which we gather to tell stories." (Al Shalloway, 2011)

"The storytelling mind is allergic to uncertainty, randomness, and coincidence. It is addicted to meaning. If the storytelling mind cannot find meaningful patterns in the world, it will try to impose them. In short, the storytelling mind is a factory that churns out true stories when it can, but will manufacture lies when it can't." (Jonathan Gottschall, "The Storytelling Animal: How Stories Make Us Human", 2012)

"We are, as a species, addicted to story. Even when the body goes to sleep, the mind stays up all night, telling itself stories." (Jonathan Gottschall, "The Storytelling Animal", 2012)

"The fact of storytelling hints at a fundamental human unease, hints at human imperfection. Where there is perfection there is no story to tell." (Ben Okri, "A Way of Being Free", 2014)

"There is no such thing as a fact. There is only how you saw the fact, in a given moment. How you reported the fact. How your brain processed that fact. There is no extrication of the storyteller from the story." (Jodi Picoult, "Small Great Things", 2016)

"Stories can begin with a question or line of inquiry." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Data storytelling provides a bridge between the worlds of logic and emotion. A data story offers a safe passage for your insights to travel around emotional pitfalls and through analytical resistance that typically impede facts." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

📉Graphical Representation: Proximity (Just the Quotes)

"If a chart contains a number of series which vary widely in individual magnitude, optical distortion may result from the necessarily sharp changes in the angle of the curves. The space between steeply rising or falling curves always appears narrower than the vertical distance between the plotting points." (Rufus R Lutz, "Graphic Presentation Simplified", 1949)

"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)

"In form, the ratio chart differs from the arithmetic chart in that the vertical scale is not divided into equal spaces to represent equal amounts, but is divided logarithmically to represent percentages of gain or loss. On the arithmetic chart equal vertical distances represent equal amounts of change; on the ratio chart equal vertical distances represent equal percentages of change." (Walter E Weld, "How to Chart; Facts from Figures with Graphs", 1959)

"Pie charts have weaknesses and dangers inherent in their design and application. First, it is generally inadvisable to attempt to portray more than four or five categories in a circle chart, especially if several small sectors are of approximately the same size.  It may be very confusing to differentiate the relative values. Secondly, the pie chart loses effectiveness if an effort is made to compare the component values of several circles, as might occur in a temporal or geographical series. [...] Thirdly, although values are measured by distances along the arc of the circle, there is a tendency to estimate values in terms of areas by size of angle. The 100-percent bar chart is often preferable to the circle chart's angle and area comparison as it is easier to divide into parts, more convenient to use, has sections that may be shaded for contrast with grouping possible by bracketing, and has an easily readable percentage scale outside the bars." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"The fact that index numbers attempt to measure changes of items gives rise to some knotty problems. The dispersion of a group of products increases with the passage of time, principally because some items have a long-run tendency to fall while others tend to rise. Basic changes in the demand is fundamentally responsible. The averages become less and less representative as the distance from the period increases." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"There are several classes of flowcharts used in recording study data in the Workbook. The purpose of any chart; of course, is to clarify and to make the information more understandable. One of these types of charts is a Process Flow Chart. It concerns itself with the flow of physical materials, including documents, through a system, especially in terms of distance and time. It is most useful in analyzing some of the cost and benefit factors for existing and proposed systems. System flowcharts [...] have been called the analyst's 'shorthand'. They can be forms-oriented or task-oriented. These flowcharts are not only the primary way of recording data pertinent to the current system, but are used for developing and displaying the new system as well. Later, in the implementation phase, program flowcharts, a fundamental tool of programming, would be developed." (Robert D Carlsen, "The Systems Analysis Workbook: A complete guide to project implementation and control", 1973)

"The circle graph, or pie chart, appears to simple and 'nonstatistical', so it is a popular form of presentation for general readers. However, since the eye can compare linear distances more easily and accurately than angles or areas, the component parts of a total usually can be shown more effectively in a chart using linear measurement." (Peter H Selby, "Interpreting Graphs and Tables", 1976)

"The plotted points on a graph should always be made to stand out well. They are, after all, the most important feature of a graph, since any lines linking them are nearly always a matter of conjecture. These lines should stop just short of the plotted points so that the latter are emphasised by the space surrounding them. Where a point happens to fall on an axis line, the axis should be broken for a short distance on either side of the point." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"When magnitudes are graphed on a logarithmic scale, percents and factors are easier to judge since equal multiplicative factors and percents result in equal distances throughout the entire scale." (William S Cleveland, "The Elements of Graphing Data", 1985)

"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)

"Visually, skewed sample distributions have one 'longer' and one 'shorter' tail. More general terms are 'heavier' and 'lighter' tails. Tail weight reflects not only distance from the center" (tail length) but also the frequency of cases at that distance" (tail depth, in a histogram). Tail weight corresponds to actual weight if the sample histogram were cut out of wood and balanced like a seesaw on its median" (see next section). A positively skewed distribution is heavier to the right of the median; negative skew implies the opposite." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"If you want to show the growth of numbers which tend to grow by percentages, plot them on a logarithmic vertical scale. When plotted against a logarithmic vertical axis, equal percentage changes take up equal distances on the vertical axis. Thus, a constant annual percentage rate of change will plot as a straight line. The vertical scale on a logarithmic chart does not start at zero, as it shows the ratio of values" (in this case, land values), and dividing by zero is impossible." (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)

"Distance and detection also play a role in our ability to decode information from graphs. The closer together objects are, the easier it is to judge attributes that compare them. As distance between objects increases, accuracy of judgment decreases. It is certainly easier to judge the difference in lengths of two bars if they are next to one another than if they are pages apart." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"[…] 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 isa mistake – maybe due to a measuring or recording error." (Alan Graham, "Developing Thinking in Statistics", 2006)

"[...] without conscious effort, the brain always tries to close the distance between observed phenomena and knowledge or wisdom that can help us survive. This is what cognition means. The role of an information architect is to anticipate this process and generate order before people’s brains try to do it on their own." (Alberto Cairo, "The Functional Art", 2011)

"The tricky part is that there aren’t really any hard- and- fast rules when it comes to identifying outliers. Some economists say an outlier is anything that’s a certain distance away from the mean, but in practice it’s fairly subjective and open to interpretation. That’s why statisticians spend so much time looking at data on a case-by-case basis to determine what is - and isn’t - an outlier." (John H Johnson & Mike Gluck, "Everydata: The misinformation hidden in the little data you consume every day", 2016)

"Charts abstract information. They make it easier to see patterns at a distance, compare, and extrapolate. Icon encodings are graphical elements that are often used to visually represent the semantic meaning of marks for categorical data. Assigning meaningful icons to display elements helps the user perceive and interpret the visualization easier." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

📉Graphical Representation: Line Graphs (Just the Quotes)

"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)

"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)

"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)

"While circle charts are not likely to present especially new or creative ideas, they do help the user to visualize relationships. The relationships depicted by circle charts do not tend to be very complex, in contrast to those of some line graphs. Normally, the circle chart is used to portray a common type of relationship (namely. part-to-total) in an attractive manner and to expedite the message transfer from designer to user." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"There are several uses for which the line graph is particularly relevant. One is for a series of data covering a long period of time. Another is for comparing several series on the same graph. A third is for emphasizing the movement of data rather than the amount of the data. It also can be used with two scales on the vertical axis, one on the right and another on the left, allowing different series to use different scales, and it can be used to present trends and forecasts." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"In the case of graphs, the number of lines which can be included on any one illustration will depend largely on how close the lines are and how often they cross one another. Three or four is likely to be the maximum acceptable number. In some instances, there may be an argument for using several graphs with one line each as opposed to one graph with multiple lines. It has been shown that these two arrangements are equally satisfactory if the user wishes to read off the value of specific points; if, however, he wishes to compare the lines, than the single multi-line graph is superior." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 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)

"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)

"The biggest difference between line graphs and sparklines is that a sparkline is compact with no grid lines. It isnʼt meant to give precise values; rather, it should be considered just like any other word in the sentence. Its general shape acts as another term and lends additional meaning in its context. The driving forces behind these compact sparklines are speed and convenience." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"The ability to see meaningful shapes in the data represents the highest level of data visualization, because it represents the highest level of data integration and a richer graphical landscape. Line charts and scatter plots are frequently used for this shape visualization." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

"The law of continuity states that we interpret images so as not to generate abrupt transitions or otherwise create images that are more complex. […] we can arbitrarily fill in the missing elements to complete a pattern. It’s also the case of time series, in which we assume that data points in the future will be a smooth continuation of the past. […] In a line chart, those series with a similar slope (that is, they appear to follow the same direction) are understood as belonging to the same group." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)

📉Graphical Representation: Relations (Just the Quotes)

"While circle charts are not likely to present especially new or creative ideas, they do help the user to visualize relationships. The relationships depicted by circle charts do not tend to be very complex, in contrast to those of some line graphs. Normally, the circle chart is used to portray a common type of relationship (namely. part-to-total) in an attractive manner and to expedite the message transfer from designer to user." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Logging skewed variables also helps to reveal the patterns in the data. […] the rescaling of the variables by taking logarithms reduces the nonlinearity in the relationship and removes much of the clutter resulting from the skewed distributions on both variables; in short, the transformation helps clarify the relationship between the two variables. It also […] leads to a theoretically meaningful regression coefficient." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"If you want to dramatize comparisons in relation to the whole. use a pie chart. If you want to add coherence to the narrative, the pie chart also helps because it depicts a whole. If your main interest is in stressing the relationship of one factor to another, use bar charts. If you wish to achieve all these effects. you can use either type of chart. and decide on the basis of which one is more aesthetically or pictorially interesting." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Understandability implies that the graph will mean something to the audience. If the presentation has little meaning to the audience, it has little value. Understandability is the difference between data and information. Data are facts. Information is facts that mean something and make a difference to whoever receives them. Graphic presentation enhances understanding in a number of ways. Many people find that the visual comparison and contrast of information permit relationships to be grasped more easily. Relationships that had been obscure become clear and provide new insights." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 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 emphasize, 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)

"Statistics are numerical statements of facts in any department of inquiry, placed in relation to each other; statistical methods are devices for abbreviating and classifying the statements and making clear the relations." (Arthur L Bowley, "An Elementary Manual of Statistics", 1934)

"Scatter charts show the relationships between information, plotted as points on a grid. These groupings can portray general features of the source data, and are useful for showing where correlationships occur frequently. Some scatter charts connect points of equal value to produce areas within the grid which consist of similar features." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Area graphs are generally not used to convey specific values. Instead, they are most frequently used to show trends and relationships, to identify and/or add emphasis to specific information by virtue of the boldness of the shading or color, or to show parts-of-the-whole." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"A good graph displays relationships and structures that are difficult to detect by merely looking at the data." (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)

"Three key aspects of presenting high dimensional data are: rendering, manipulation, and linking. Rendering determines what is to be plotted, manipulation determines the structure of the relationships, and linking determines what information will be shared between plots or sections of the graph." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Color can tell us where to look, what to compare and contrast, and it can give us a visual scale of measure. Because color can be so effective, it is often used for multiple purposes in the same graphic - which can create graphics that are dazzling but difficult to interpret. Separating the roles that color can play makes it easier to apply color specifically for encouraging different kinds of visual thinking. [...] Choose colors to draw attention, to label, to show relationships (compare and contrast), or to indicate a visual scale of measure." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"A scatterplot reveals the strength and shape of the relationship between a pair of variables. A scatterplot represents the two variables by axes drawn at right angles to each other, showing the observations as a cloud of points, each point located according to its values on the two variables. Various lines can be added to the plot to help guide our search for understanding." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"A good chart can tell a story about the data, helping you understand relationships among data so you can make better decisions. The wrong chart can make a royal mess out of even the best data set." (John H Johnson & Mike Gluck, "Everydata: The misinformation hidden in the little data you consume every day", 2016)

"Essentially, magnitude is the size of the effect. It’s a way to determine if the results are meaningful. Without magnitude, it’s hard to get a sense of how much something matters. […] the magnitude of an effect can change, depending on the relationship." (John H Johnson & Mike Gluck, "Everydata: The misinformation hidden in the little data you consume every day", 2016)

"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)

"[...] scatterplots had advantages over earlier graphic forms: the ability to see clusters, patterns, trends, and relations in a cloud of points. Perhaps most importantly, it allowed the addition of visual annotations (point symbols, lines, curves, enclosing contours, etc.) to make those relationships more coherent and tell more nuanced stories." (Michael Friendly & Howard Wainer, "A History of Data Visualization and Graphic Communication", 2021)

📉Graphical Representation: Figures (Just the Quotes)

"Information that is imperfectly acquired, is generally as imperfectly retained; and a man who has carefully investigated a printed table, finds, when done, that he has only a very faint and partial idea of what he has read; and that like a figure imprinted on sand, is soon totally erased and defaced." (William Playfair, "The Commercial and Political Atlas", 1786)

"When a law is contained in figures, it is buried like metal in an ore; it is necessary to extract it. This is the work of graphical representation. It points out the coincidences, the relationships between phenomena, their anomalies, and we have seen what a powerful means of control it puts in the hands of the statistician to verify new data, discover and correct errors with which they have been stained." (Emile Cheysson, "Les methods de la statistique", 1890)

"The graphical method has considerable superiority for the exposition of statistical facts over the tabular. A heavy bank of figures is grievously wearisome to the eye, and the popular mind is as incapable of drawing any useful lessons from it as of extracting sunbeams from cucumbers." (Arthur B Farquhar & Henry Farquhar, "Economic and Industrial Delusions", 1891)

"The visible figures by which principles are illustrated should, so far as possible, have no accessories. They should be magnitudes pure and simple, so that the thought of the pupil may not be distracted, and that he may know what features of the thing represented he is to pay attention to." (National Education Association, 1894)

"By [diagrams] it is possible to present at a glance all the facts which could be obtained from figures as to the increase, fluctuations, and relative importance of prices, quantities, and values of different classes of goods and trade with various countries; while the sharp irregularities of the curves give emphasis to the disturbing causes which produce any striking change." (Arthur L Bowley, "A Short Account of England's Foreign Trade in the Nineteenth Century, its Economic and Social Results", 1905)

"Graphical statistics can be defined as: 'the expression of statistical facts by means of geometric processes' (Levasseur). Its general usefulness consists of replacing figures which, by their multiplicity, confuse memory, with a figure whose general appearance can be discovered all at once and, by speaking to the eyes, is more easily engraved in the memory." (Armand Julin, "Summary for a Course of Statistics, General and Applied", 1910)

"The essential quality of graphic representations is clarity. If the diagram fails to give a clearer impression than the tables of figures it replaces, it is useless. To this end, we will avoid complicating the diagram by including too much data." (Armand Julin, "Summary for a Course of Statistics, General and Applied", 1910)

"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) 

"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)

"Graphical methods comprise all those methods of representing the relations of objects or facts by means of the relations between the lines of a diagram. All devices for representing by geometrical figures the numerical data which result from the quantitative investigation of phenomena are included under this title." (William C Marshall, "Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)

"Percentages offer a fertile field for confusion. And like the ever-impressive decimal they can lend an aura of precision to the inexact. […] Any percentage figure based on a small number of cases is likely to be misleading. It is more informative to give the figure itself. And when the percentage is carried out to decimal places, you begin to run the scale from the silly to the fraudulent." (Darell Huff, "How to Lie with Statistics", 1954)

"The precision of a number is the degree of exactness with which it is stated, while the accuracy of a number is the degree of exactness with which it is known or observed. The precision of a quantity is reported by the number of significant figures in it." (Edmund C Berkeley & Lawrence Wainwright, Computers: Their Operation and Applications", 1956)

"A drawing can show a true picture of both the situation as a whole and its separate components at a glance, and do the job better than could figures or the spoken word. In its essence, a chart is a medium of communication conveying a thought, an idea, a situation from one mind to another and not a work of art or a statistical table. The simpler, the more direct it is, the better it will perform that service which is its sole function." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"Indeed the language of statistics is rarely as objective as we imagine. The way statistics are presented, their arrangement in a particular way in tables, the juxtaposition of sets of figures, in itself reflects the judgment of the author about what is significant and what is trivial in the situation which the statistics portray." (Ely Devons, "Essays in Economics", 1961)

"The art of using the language of figures correctly is not to be over-impressed by the apparent air of accuracy, and yet to be able to take account of error and inaccuracy in such a way as to know when, and when not, to use the figures. This is a matter of skill, judgment, and experience, and there are no rules and short cuts in acquiring this expertness." (Ely Devons, "Essays in Economics", 1961)

"Tables are [...] the backbone of most statistical reports. They provide the basic substance and foundation on which conclusions can be based. They are considered valuable for the following reasons: (1) Clarity - they present many items of data in an orderly and organized way. (2) Comprehension - they make it possible to compare many figures quickly. (3) Explicitness - they provide actual numbers which document data presented in accompanying text and charts. (4) Economy - they save space, and words. (5) Convenience - they offer easy and rapid access to desired items of information." (Peter H Selby, "Interpreting Graphs and Tables", 1976)

"We need [graphic] techniques because figures do not speak for them. selves. Numbers alone seldom make a convincing case or polish their author's image - the twin goals of that other great mind bender, rhetoric. While rhetoric deals in qualitative argument, its quantitative equivalent is graphics. As rhetoric has declined in popularity, so graphics have risen along with our acceptance of quantitative arguments. In graphics, figures finally find their own means of expression." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

📉Graphical Representation: Ability (Just the Quotes)

"Our inability to measure important factors does not mean either that we should sweep those factors under the rug or that we should give them all the weight in a decision. Some important factors in some problems can be assessed quantitatively. And even though thoughtful and imaginative efforts have sometimes turned the 'unmeasurable' into a useful number, some important factors are simply not measurable. As always, every bit of the investigator's ingenuity and good judgment must be brought into play. And, whatever un- knowns may remain, the analysis of quantitative data nonetheless can help us learn something about the world - even if it is not the whole story." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The types of graphics used in operating a business fall into three main categories: diagrams, maps, and charts. Diagrams, such as organization diagrams, flow diagrams, and networks, are usually intended to graphically portray how an activity should be, or is being, accomplished, and who is responsible for that accomplishment. Maps such as route maps, location maps, and density maps, illustrate where an activity is, or should be, taking place, and what exists there. [...] Charts such as line charts, column charts, and surface charts, are normally constructed to show the businessman how much and when. Charts have the ability to graphically display the past, present, and anticipated future of an activity. They can be plotted so as to indicate the current direction that is being followed in relationship to what should be followed. They can indicate problems and potential problems, hopefully in time for constructive corrective action to be taken." (Robert D Carlsen & Donald L Vest, "Encyclopedia of Business Charts", 1977)

"Graphic forms help us to perform and influence two critical functions of the mind: the gathering of information and the processing of that information. Graphs and charts are ways to increase the effectiveness and the efficiency of transmitting information in a way that enhances the reader's ability to process that information. Graphics are tools to help give meaning to information because they go beyond the provision of information and show relationships, trends, and comparisons. They help to distinguish which numbers and which ideas are more important than others in a presentation." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"The more complex the shape of any object. the more difficult it is to perceive it. The nature of thought based on the visual apprehension of objective forms suggests, therefore, the necessity to keep all graphics as simple as possible. Otherwise, their meaning will be lost or ambiguous, and the ability to convey the intended information and to persuade will be inhibited." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Graphs can present internal accounting data effectively. Because one of the main functions of the accountant is to communicate accounting information to users. accountants should use graphs, at least to the extent that they clarify the presentation of accounting data. present the data fairly, and enhance management's ability to make a more informed decision. It has been argued that the human brain can absorb and understand images more easily than words and numbers, and, therefore, graphs may be better communicative devices than written reports or tabular statements." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"The real value of dashboard products lies in their ability to replace hunt‐and‐peck data‐gathering techniques with a tireless, adaptable, information‐flow mechanism. Dashboards transform data repositories into consumable information." (Gregory L Hovis, "Stop Searching for Information Monitor it with Dashboard Technology," DM Direct, 2002)

"Distance and detection also play a role in our ability to decode information from graphs. The closer together objects are, the easier it is to judge attributes that compare them. As distance between objects increases, accuracy of judgment decreases. It is certainly easier to judge the difference in lengths of two bars if they are next to one another than if they are pages apart." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"For a visual to qualify as beautiful, it must be aesthetically pleasing, yes, but it must also be novel, informative, and efficient. [...] For a visual to truly be beautiful, it must go beyond merely being a conduit for information and offer some novelty: a fresh look at the data or a format that gives readers a spark of excitement and results in a new level of understanding. Well-understood formats" (e.g., scatterplots) may be accessible and effective, but for the most part they no longer have the ability to surprise or delight us. Most often, designs that delight us do so not because they were designed to be novel, but because they were designed to be effective; their novelty is a byproduct of effectively revealing some new insight about the world." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)

"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)

"The key to the success of any visual, beautiful or not, is providing access to information so that the user may gain knowledge. A visual that does not achieve this goal has failed. Because it is the most important factor in determining overall success, the ability to convey information must be the primary driver of the design of a visual." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)

"It is important to remember that a visual representation of a scientific concept (or data) is a re-presentation, and not the thing itself - some interpretation or translation is always involved. There are many parallels between creating a graphic and writing an article. First, you must carefully plan what to 'say', and in what order you will 'say it'. Then you must make judgments to determine a hierarchy of information - what must be included and what could be left out? The process of making a visual representation requires you to clarify your thinking and improve your ability to communicate with others. Furthermore, the process of making an effective graphic often leads to new insights into your work; when you make decisions about how to depict your data and underlying concepts, you must often clarify your basic assumptions." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"Processes take place over time and result in change. However, we’re often constrained to depict processes in static graphics, perhaps even a single image. Luckily, a good static graphic can be just as successful, perhaps even more so, than an animation. Giving the reader the ability to see each 'frame' of time can offer a valuable perspective." (Felice C Frankel & Angela H DePace, "Visual Strategies", 2012)

"We have an inbuilt ability to manipulate visual metaphors in ways we cannot do with the things and concepts they stand for - to use them as malleable, conceptual Tetris blocks or modeling clay that we can more easily squeeze, stack, and reorder. And then - whammo! - a pattern emerges, and we’ve arrived someplace we would never have gotten by any other means." (Zach Gemignani et al, "Data Fluency", 2014)

"Upon discovering a visual image, the brain analyzes it in terms of primitive shapes and colors. Next, unity contours and connections are formed. As well, distinct variations are segmented. Finally, the mind attracts active attention to the significant things it found. That process is permanently running to react to similarities and dissimilarities in shapes, positions, rhythms, colors, and behavior. It can reveal patterns and pattern-violations among the hundreds of data values. That natural ability is the most important thing used in diagramming." (Vasily Pantyukhin, "Principles of Design Diagramming", 2015)

"Before you can even consider creating a data story, you must have a meaningful insight to share. One of the essential attributes of a data story is a central or main insight. Without a main point, your data story will lack purpose, direction, and cohesion. A central insight is the unifying theme" (telos appeal) that ties your various findings together and guides your audience to a focal point or climax for your data story. However, when you have an increasing amount of data at your disposal, insights can be elusive. The noise from irrelevant and peripheral data can interfere with your ability to pinpoint the important signals hidden within its core." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Communication requires the ability to expand or contract a message based on norms within a given culture or language. Expansion provides more detail, sometimes adding in information that is culturally relevant or needed for the person to understand. Contraction preserves the same intent but discards information that isn't needed by that person. Some concepts in certain situations require greater detail than others." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Understanding language goes hand in hand with the ability to integrate complex contextual information into an effective visualization and being able to converse with the data interactively, a term we call analytical conversation. It also helps us think about ways to create artifacts that support and manage how we converse with machines as we see and understand data.(Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

📉Graphical Representation: Multivariate Data (Just the Quotes)

"An especially effective device for enhancing the explanatory power of time-series displays is to add spatial dimensions to the design of the graphic, so that the data are moving over space" (in two or three dimensions) as well as over time. […] Occasionally graphics are belligerently multivariate, advertising the technique rather than the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Graphical excellence is the well-designed presentation of interesting data - a matter of substance, of statistics, and of design. Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency. Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space. Graphical excellence is nearly always multivariate. And graphical excellence requires telling the truth about the data." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"At the heart of quantitative reasoning is a single question: Compared to what? Small multiple designs, multivariate and data bountiful, answer directly by visually enforcing comparisons of changes, of the differences among objects, of the scope of alternatives. For a wide range of problems in data presentation, small multiples are the best design solution." (Edward R Tufte, "Envisioning Information", 1990)

"Show multivariate data; that is, show more than 1 or 2 variables." (Edward R Tufte, "Beautiful Evidence", 2006)

"The purpose of an evidence presentation is to assist thinking. Thus presentations should be constructed so as to assist with the fundamental intellectual tasks in reasoning about evidence: describing the data, making multivariate comparisons, understanding causality, integrating a diversity of evidence, and documenting the analysis. Thus the Grand Principle of analytical design: 'The principles of analytical design are derived from the principles of analytical thinking.' Cognitive tasks are turned into principles of evidence presentation and design." (Edward R Tufte, "Beautiful Evidence", 2006)

"Multivariate techniques often summarize or classify many variables to only a few groups or factors (e.g., cluster analysis or multi-dimensional scaling). Parallel coordinate plots can help to investigate the influence of a single variable or a group of variables on the result of a multivariate procedure. Plotting the input variables in a parallel coordinate plot and selecting the features of interest of the multivariate procedure will show the influence of different input variables." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Parallel coordinate plots are often overrated concerning their ability to depict multivariate features. Scatterplots are clearly superior in investigating the relationship between two continuous variables and multivariate outliers do not necessarily stick out in a parallel coordinate plot. Nonetheless, parallel coordinate plots can help to find and understand features such as groups/clusters, outliers and multivariate structures in their multivariate context. The key feature is the ability to select and highlight individual cases or groups in the data, and compare them to other groups or the rest of the data." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Given the important role that correlation plays in structural equation modeling, we need to understand the factors that affect establishing relationships among multivariable data points. The key factors are the level of measurement, restriction of range in data values" (variability, skewness, kurtosis), missing data, nonlinearity, outliers, correction for attenuation, and issues related to sampling variation, confidence intervals, effect size, significance, sample size, and power." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"A heatmap is a visualization where values contained in a matrix are represented as colors or color saturation. Heatmaps are great for visualizing multivariate data" (data in which analysis is based on more than two variables per observation), where categorical variables are placed in the rows and columns and a numerical or categorical variable is represented as colors or color saturation." (Mario Döbler & Tim Großmann, "The Data Visualization Workshop", 2nd Ed., 2020)

📉Graphical Representation: Opportunities (Just the Quotes)

"Charts offer opportunities to distort information, to misinform. An old adage can be extended to read: 'There are lies, damned lies, statistics and charts'. Our visual impressions are often more memorable than our understanding of the facts they describe. [...] Never let your design enthusiasms overrule your judgement of the truth." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Graphical illustrations should be simple and pleasing to the eye, but the presentation must remain scientific. In other words, we want to avoid those graphical features that are purely decorative while keeping a critical eye open for opportunities to enhance the scientific inference we expect from the reader. A good graphical design should maximize the proportion of the ink used for communicating scientific information in the overall display." (Phillip I Good & James W Hardin, "Common Errors in Statistics (and How to Avoid Them)", 2003)

"If we attempt to map the world of a story before we explore it, we are likely either to (a) prematurely limit our exploration, so as to reduce the amount of material we need to consider, or (b) explore at length but, recognizing the impossibility of taking note of everything, and having no sound basis for choosing what to include, arbitrarily omit entire realms of information. The opportunities are overwhelming." (Peter Turchi, "Maps of the Imagination: The writer as cartographer", 2004)

"All graphics by definition employ metaphors, but some are more metaphorical than others. Sometimes the metaphor escapes from its graphical cage, takes on a life of its own and provides exciting deception opportunities." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"Category definition and selection in the pre-graphical phase of communication offer varied manipulation opportunities. But once we get to designing the chart itself category distortion opportunities are even more attractive." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"Design has the power to enrich our lives by engaging our emotions through image, form, texture, color, sound, and smell. The intrinsically human-centered nature of design thinking points to the next step: we can use our empathy and understanding of people to design experiences that create opportunities for active engagement and participation." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Competition for your audiences attention is fierce. The fact that infographics are unique allows organizations an opportunity to make the content they are publishing stand out and get noticed." (Mark Smiciklas, "The Power of Inforgraphics", 2012)

"Sparklines aren't necessarily a variation on the line chart, rather, a clever use of them. [...] They take advantage of our visual perception capabilities to discriminate changes even at such a low resolution in terms of size. They facilitate opportunities to construct particularly dense visual displays of data in small space and so are particularly applicable for use on dashboards." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"The process of visual analysis can potentially go on endlessly, with seemingly infinite combinations of variables to explore, especially with the rich opportunities bigger data sets give us. However, by deploying a disciplined and sensible balance between deductive and inductive enquiry you should be able to efficiently and effectively navigate towards the source of the most compelling stories." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Data storytelling gives your insight the best opportunity to capture attention, be understood, be remembered, and be acted on. An effective data story helps your insight reach its full potential: inspiring others to act and drive change." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

📉Graphical Representation: Reliability (Just the Quotes)

"Most authors would greatly resent it if they were told that their writings contained great exaggerations, yet many of these same authors permit their work to be illustrated with charts which are so arranged as to cause an erroneous interpretation. If authors and editors will inspect their charts as carefully as they revise their written matter, we shall have, in a very short time, a standard of reliability in charts and illustrations just as high as now found in the average printed page." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919) 

"Reliability is highly valued by accountants and has been defined as 'the faithfulness with which it (information) represents what it purports to represent'. The reason reliability is so important is that an essential characteristic of an accounting report is its acceptance, and if a report is considered to be misleading or superfluous, it and future reports will be disregarded." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"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)

"In everyday life, 'estimation' means a rough and imprecise procedure leading to a rough and imprecise result. You 'estimate' when you cannot measure exactly. In statistics, on the other hand, 'estimation' is a technical term. It means a precise and accurate procedure, leading to a result which may be imprecise, but where at least the extent of the imprecision is known. It has nothing to do with approximation. You have some data, from which you want to draw conclusions and produce a 'best' value for some particular numerical quantity (or perhaps for several quantities), and you probably also want to know how reliable this value is, i.e. what the error is on your estimate." (Roger J Barlow, "Statistics: A guide to the use of statistical methods in the physical sciences", 1989)

"Pie charts have severe perceptual problems. Experiments in graphical perception have shown that compared with dot charts, they convey information far less reliably. But if you want to display some data, and perceiving the information is not so important, then a pie chart is fine." (Richard Becker & William S Cleveland," S-Plus Trellis Graphics User's Manual", 1996)

"Data visualization is a means to an end, not an end in itself. It's merely a bridge connecting the messenger to the receiver and its limitations are framed by our own inherent irrationalities, prejudices, assumptions, and irrational tastes. All these factors can undermine the consistency and reliability of any predicted reaction to a given visualization, but that is something we can't realistically influence." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Are your insights based on data that is accurate and reliable? Trustworthy data is correct or valid, free from significant defects and gaps. The trustworthiness of your data begins with the proper collection, processing, and maintenance of the data at its source. However, the reliability of your numbers can also be influenced by how they are handled during the analysis process. Clean data can inadvertently lose its integrity and true meaning depending on how it is analyzed and interpreted." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Without knowing the source and context, a particular statistic is worth little. Yet numbers and statistics appear rigorous and reliable simply by virtue of being quantitative, and have a tendency to spread." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)