📉Graphical Representation: Numbers (Just the Quotes)

"They [diagrams] are designed not so much to allow of reference to particular numbers, which can be better had from printed tables of figures, as to exhibit to the eye the general results of large masses of figures which it is hopeless to attack in any other way than by graphical representation." (William S Jevons, [letter to Richard Hutton] 1863)

"If statistical graphics, although born just yesterday, extends its reach every day, it is because it replaces long tables of numbers and it allows one not only to embrace at glance the series of phenomena, but also to signal the correspondences or anomalies, to find the causes, to identify the laws." (Émile Cheysson, cca. 1877)

"When numbers in tabular form are taboo and words will not do the work well as is often the case. There is one answer left: Draw a picture. About the simplest kind of statistical picture or graph, is the line variety. It is very useful for showing trends, something practically everybody is interested in showing or knowing about or spotting or deploring or forecasting." (Darell Huff, "How to Lie with Statistics", 1954)

"Graphic charts are ways of presenting quantitative as well as qualitative information in an efficient and effective visual form. Numbers and ideas presented graphically are often more easily understood. remembered. and integrated than when they are presented in narrative or tabular form. Descriptions. trends. relationships, and comparisons can be made more apparent. Less time is required to present and comprehend information when graphic methods are employed. As the old truism states, 'One picture is worth a thousand words.'" (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Inept graphics also flourish because many graphic artists believe that statistics are boring and tedious. It then follows that decorated graphics must pep up, animate, and all too often exaggerate what evidence there is in the data. […] If the statistics are boring, then you've got the wrong numbers." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers even a very large set - is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The essence of a graphic display is that a set of numbers having both magnitudes and an order are represented by an appropriate visual metaphor - the magnitude and order of the metaphorical representation match the numbers. We can display data badly by ignoring or distorting this concept." (Howard Wainer, "How to Display Data Badly", The American Statistician Vol. 38(2), 1984) 

"Lurking behind chartjunk is contempt both for information and for the audience. Chartjunk promoters imagine that numbers and details are boring, dull, and tedious, requiring ornament to enliven. Cosmetic decoration, which frequently distorts the data, will never salvage an underlying lack of content. If the numbers are boring, then you've got the wrong numbers." (Edward R Tufte, "Envisioning Information", 1990)

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

"Not all statistics start out bad, but any statistic can be made worse. Numbers - even good numbers - can be misunderstood or misinterpreted. Their meanings can be stretched, twisted, distorted, or mangled. These alterations create what we can call mutant statistics - distorted versions of the original figures." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"Not all statistics start out bad, but any statistic can be made worse. Numbers - even good numbers - can be misunderstood or misinterpreted. Their meanings can be stretched, twisted, distorted, or mangled. These alterations create what we can call mutant statistics - distorted versions of the original figures." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"Statistics can certainly pronounce a fact, but they cannot explain it without an underlying context, or theory. Numbers have an unfortunate tendency to supersede other types of knowing. […] Numbers give the illusion of presenting more truth and precision than they are capable of providing." (Ronald J Baker, "Measure what Matters to Customers: Using Key Predictive Indicators", 2006)

"We need [graphic] techniques because figures do not speak for themselves. 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)

"Another way to obscure the truth is to hide it with relative numbers. […] Relative scales are always given as percentages or proportions. An increase or decrease of a given percentage only tells us part of the story, however. We are missing the anchoring of absolute values." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"By giving numbers a proper shape, by visually encoding them, the graphic has saved you time and energy that you would otherwise waste if you had to use a table that was not designed to aid your mind." (Alberto Cairo, "The Functional Art", 2011)

"The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)

"A common mistake is that all visualization must be simple, but this skips a step. You should actually design graphics that lend clarity, and that clarity can make a chart 'simple' to read. However, sometimes a dataset is complex, so the visualization must be complex. The visualization might still work if it provides useful insights that you wouldn’t get from a spreadsheet. […] Sometimes a table is better. Sometimes it’s better to show numbers instead of abstract them with shapes. Sometimes you have a lot of data, and it makes more sense to visualize a simple aggregate than it does to show every data point." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)

"Statistics, because they are numbers, appear to us to be cold, hard facts. It seems that they represent facts given to us by nature and it’s just a matter of finding them. But it’s important to remember that people gather statistics. People choose what to count, how to go about counting, which of the resulting numbers they will share with us, and which words they will use to describe and interpret those numbers. Statistics are not facts. They are interpretations. And your interpretation may be just as good as, or better than, that of the person reporting them to you." (Daniel J Levitin, "Weaponized Lies", 2017)

"Numbers are ideal vehicles for promulgating bullshit. They feel objective, but are easily manipulated to tell whatever story one desires. Words are clearly constructs of human minds, but numbers? Numbers seem to come directly from Nature herself. We know words are subjective. We know they are used to bend and blur the truth. Words suggest intuition, feeling, and expressivity. But not numbers. Numbers suggest precision and imply a scientific approach. Numbers appear to have an existence separate from the humans reporting them." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

📉Graphical Representation: Series (Just the Quotes)

"If statistical graphics, although born just yesterday, extends its reach every day, it is because it replaces long tables of numbers and it allows one not only to embrace at glance the series of phenomena, but also to signal the correspondences or anomalies, to find the causes, to identify the laws." (Émile Cheysson, cca. 1877)

"In any chart where index numbers are used the greatest care should be taken to select as unity a set of conditions thoroughly typical and representative. It is frequently best to take as unity the average of a series of years immediately preceding the years for which a study is to be made. The series of years averaged to represent unity should, if possible, be so selected that they will include one full cycle or wave of fluctuation. If one complete cycle involves too many years, the years selected as unity should be taken in equal number on either side of a year which represents most nearly the normal condition." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"A graph is a pictorial representation or statement of a series of values all drawn to scale. It gives a mental picture of the results of statistical examination in one case while in another it enables calculations to be made by drawing straight lines or it indicates a change in quantity together with the rate of that change. A graph then is a picture representing some happenings and so designed as to bring out all points of significance in connection with those happenings. When the curve has been plotted delineating these happenings a general inspection of it shows the essential character of the table or formula from which it was derived." (William C Marshall, "Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)

"A series ot quantities or values can be most simply and often best shown by a series of corresponding lines or bars. All bars being drawn against one and the same scale, their lengths vary with the amounts which they represent." (Karl G Karsten, "Charts and Graphs", 1925)

"Although, the tabular arrangement is the fundamental form for presenting a statistical series, a graphic representation - in a chart or diagram - is often of great aid in the study and reporting of statistical facts. Moreover, sometimes statistical data must be taken, in their sources, from graphic rather than tabular records." (William L Crum et al, "Introduction to Economic Statistics", 1938)

"[….] double-scale charts are likely to be misleading unless the two zero values coincide" (either on or off the chart). To insure an accurate comparison of growth the scale intervals should be so chosen that both curves meet at some point. This treatment produces the effect of percentage relatives or simple index numbers with the point of juncture serving as the base point. The principal advantage of this form of presentation is that it is a short-cut method of comparing the relative change of two or more series without computation. It is especially useful for bringing together series that either vary widely in magnitude or are measured in different units and hence cannot be compared conveniently on a chart having only one absolute-amount scale. In general, the double scale treatment should not be used for presenting growth comparisons to the general reader." (Kenneth W Haemer, "Double Scales Are Dangerous", The American Statistician Vol. 2" (3) , 1948)

"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)

"The use of two or more amount scales for comparisons of series in which the units are unlike and, therefore, not comparable [...] generally results in an ineffective and confusing presentation which is difficult to understand and to interpret. Comparisons of this nature can be much more clearly shown by reducing the components to a comparable basis as percentages or index numbers." (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)

"Where the values of a series are such that a large part the grid would be superfluous, it is the practice to break the grid thus eliminating the unused portion of the scale, but at the same time indicating the zero line. Failure to include zero in the vertical scale is a very common omission which distorts the data and gives an erroneous visual impression." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"Besides being easier to construct than a bar chart, the line chart possesses other advantages. It is easier to read, for while the bars stand out more prominently than the line, they tend to become confusing if numerous, and especially so when they record alternate increase and decrease. It is easier for the eye to follow a line across the face of the chart than to jump from bar top to bar top, and the slope of the line connecting two points is a great aid in detecting minor changes. The line is also more suggestive of movement than arc bars, and movement is the very essence of a time series. Again, a line chart permits showing two or more related variables on the same chart, or the same variable over two or more corresponding periods." (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)

"Since bars represent magnitude by their length, the zero line must be shown and the arithmetic scale must not be broken. Occasionally an excessively long bar in a series of bars may be broken off at the end, and the amount involved shown directly beyond it, without distorting the general trend of the other bars, but this practice applies solely when only one bar exceeds the scale." (Anna C Rogers, "Graphic Charts Handbook", 1961)

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

"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)

"The bar graph and the column graph are popular because they are simple and easy to read. These are the most versatile of the graph forms. They can be used to display time series, to display the relationship between two items, to make a comparison among several items, and to make a comparison between parts and the whole" (total). They do not appear to be as 'statistical', which is an advantage to those people who have negative attitudes toward statistics. The column graph shows values over time, and the bar graph shows values at a point in time. bar graph compares different items as of a specific time" (not over time)." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"The time-series plot is the most frequently used form of graphic design. With one dimension marching along to the regular rhythm of seconds, minutes, hours, days, weeks, months, years, centuries, or millennia, the natural ordering of the time scale gives this design a strength and efficiency of interpretation found in no other graphic arrangement." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"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)

"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)

"[decision trees are the] most picturesque of all the allegedly scientific aids to making decisions. The analyst charts all the possible outcomes of different options, and charts all the latters' outcomes, too. This produces a series of stems and branches" (hence the tree). Each of the chains of events is given a probability and a monetary value." (Robert Heller, "The Pocket Manager", 1987)

"Grouped area graphs sometimes cause confusion because the viewer cannot determine whether the areas for the data series extend down to the zero axis. […] Grouped area graphs can handle negative values somewhat better than stacked area graphs but they still have the problem of all or portions of data curves being hidden by the data series towards the front." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996)

"Time-series forecasting is essentially a form of extrapolation in that it involves fitting a model to a set of data and then using that model outside the range of data to which it has been fitted. Extrapolation is rightly regarded with disfavour in other statistical areas, such as regression analysis. However, when forecasting the future of a time series, extrapolation is unavoidable." (Chris Chatfield, "Time-Series Forecasting" 2nd Ed, 2000)

"Comparing series visually can be misleading […]. Local variation is hidden when scaling the trends. We first need to make the series stationary" (removing trend and/or seasonal components and/or differences in variability) and then compare changes over time. To do this, we log the series" (to equalize variability) and difference each of them by subtracting last year’s value from this year’s value." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"In general. statistical graphics should be moderately greater in length than in height. And, as William Cleveland discovered, for judging slopes and velocities up and down the hills in time-series, best is an aspect ratio that yields hill - slopes averaging 45°, over every cycle in the time-series. Variations in slopes are best detected when the slopes are around 45°, uphill or downhill." (Edward R Tufte, "Beautiful Evidence", 2006)

"[...] if you want to show change through time, use a time-series chart; if you need to compare, use a bar chart; or to display correlation, use a scatter-plot - because some of these rules make good common sense." (Alberto Cairo, "The Functional Art", 2011)

"if you want to show change through time, use a time-series chart; if you need to compare, use a bar chart; or to display correlation, use a scatter-plot - because some of these rules make good common sense." (Alberto Cairo, "The Functional Art", 2011)

"Bubble charts are a type of area chart that use discrete or continuous data and can be used to display nominal and ranking relationships. You would seldom use them to show only a time series or part-to-whole relationship. Bubble charts can be used to compare subcategories’ values, in either side-by-side comparisons, or in more elaborate graph types such as bubble plots (when showing ranking and time series) and bubble maps (if geography was germane to the story being told). They are most valuable when the range of data set is large, and there is a good amount of variance between the smallest and the largest subcategories. They can also be useful when using bar charts simply looks awkward." (Jason Lankow et al, "Infographics: The power of visual storytelling", 2012)

"When using dot plots to show a time series relationship, the scale does not have to start at a zero baseline. For the other relationships they do, however. For a time series relationship, the scale can be truncated if there is a story worth telling in the data that would otherwise be obscured by using a very large scale. However, you should use discretion when attempting to do this; a good rule of thumb is to use a scale in which the range of the dot plots consists of two-thirds of the graph’s total height, in order to display data trends more clearly. Additionally, if your goal is to show a time series relationship with continual data, you can throw a line on it, connecting the points. Essentially, you can use a series of straight lines between the points, which will help guide the reader’s eyes from left to right." (Jason Lankow et al, "Infographics: The power of visual storytelling", 2012)

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

"With time series though, there is absolutely no substitute for plotting. The pertinent pattern might end up being a sharp spike followed by a gentle taper down. Or, maybe there are weird plateaus. There could be noisy spikes that have to be filtered out. A good way to look at it is this: means and standard deviations are based on the naïve assumption that data follows pretty bell curves, but there is no corresponding 'default' assumption for time series data" (at least, not one that works well with any frequency), so you always have to look at the data to get a sense of what’s normal. [...] Along the lines of figuring out what patterns to expect, when you are exploring time series data, it is immensely useful to be able to zoom in and out." (Field Cady, "The Data Science Handbook", 2017)

"A time series is a sequence of values, usually taken in equally spaced intervals. […] Essentially, anything with a time dimension, measured in regular intervals, can be used for time series analysis." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Heat maps are effective visualizations for seeing concentrations as well as patterns. Adding time series to a heat map can also reveal seasonality that may not be obvious otherwise." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Many statistical procedures perform more effectively on data that are normally distributed, or at least are symmetric and not excessively kurtotic" (fat-tailed), and where the mean and variance are approximately constant. Observed time series frequently require some form of transformation before they exhibit these distributional properties, for in their 'raw' form they are often asymmetric." (Terence C Mills, "Applied Time Series Analysis: A practical guide to modeling and forecasting", 2019)

"Showing the data and reducing the clutter means reducing extraneous gridlines, markers, and shades that obscure the actual data. Active titles, better labels, and helpful annotations will integrate your chart with the text around it. When charts are dense with many data series, you can use color strategically to highlight series of interest or break one dense chart into multiple smaller versions. " (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

📉Graphical Representation: Information (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)

"The advantages proposed by [the graphical] mode of representation, are to facilitate the attainment of information, and aid the memory in retaining it: which two points form the principal business in what we call learning. Of all the senses, the eye gives the liveliest and most accurate idea of whatever is susceptible of being represented to it; and when proportion between different quantities is the object, then the eye has an incalculable superiority." (William Playfair, The Statistical Breviary", 1801)

"It should be a strict rule for all kinds of curve plotting that the horizontal scale must be used. for the independent variable and the vertical scale for the dependent variable. When the curves are plotted by this rule the reader can instantly select a set of conditions from the horizontal scale and read the information from the vertical scale. If there were no rule relating to the arrangement of scales for the independent and dependent variables, the reader would never be able to tell whether he should approach a chart from the vertical scale and read the information from the horizontal scale, or the reverse." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"The greatest contribution to chart-making, from any single source, is the Gantt Progress Chart. This chart is, unquestionably, the most powerful graphic device for business and for all executive and managerial purposes. While the description has been rather full, as given herein, it is by no means complete; and the Gantt charting methods, in all their co-ordinated ramifications, constitute an independent system of accounting and of executive control,in this [...]" (Karl G Karsten, "Charts and Graphs", [preface] 1925)

"A man's judgment cannot be better than the information on which he has based it. Give him no news, or present him only with distorted and incomplete data, with ignorant, sloppy, or biased reporting, with propaganda and deliberate falsehoods, and you destroy his whole reasoning process and make him somewhat less than a man." (Arthur H Sulzberger, [speech] 1948)

"No map contains all the information about the territory it represents. [...] Furthermore, the scale of the map makes a big difference. The smaller the scale the less features will be shown." (Anatol Rapoport, "Science and the goals of man: a study in semantic orientation", 1950)

"A piece of self-deception - often dear to the heart of apprentice scientists - is the drawing of a 'smooth curve'" (how attractive it sounds!) through a set of points which have about as much trend as the currants in plum duff. Once this is done, the mind, looking for order amidst chaos, follows the Jack-o'-lantern line with scant attention to the protesting shouts of the actual points. Nor, let it be whispered, is it unknown for people who should know better to rub off the offending points and publish the trend line which their foolish imagination has introduced on the flimsiest of evidence. Allied to this sin is that of overconfident extrapolation, i.e. extending the graph by guesswork beyond the range of factual information. Whenever extrapolation is attempted it should be carefully distinguished from the rest of the graph, e.g. by showing the extrapolation as a dotted line in contrast to the full line of the rest of the graph. [...] Extrapolation always calls for justification, sooner or later. Until this justification is forthcoming, it remains a provisional estimate, based on guesswork." (Michael J Moroney, "Facts from Figures", 1951)

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

"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)

"The word data (singular: datum) refers to bits and pieces of information. such as numbers. symbols. words, pictures, gestures, or sounds. Data represent nonstructured information. In short, data are incoherent, whereas information is coherent." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Pencil and paper for construction of distributions, scatter diagrams, and run-charts to compare small groups and to detect trends are more efficient methods of estimation than statistical inference that depends on variances and standard errors, as the simple techniques preserve the information in the original data." (William E Deming, "On Probability as Basis for Action" American Statistician Vol. 29" (4), 1975)

"Just like the spoken or written word, statistics and graphs can lie. They can lie by not telling the full story. They can lead to wrong conclusions by omitting some of the important facts. [...] Always look at statistics with a critical eye, and you will not be the victim of misleading information." (Dyno Lowenstein, "Graphs", 1976)

"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)

"Most graphs used in the analysis of data consist of points arising in effect from distinct individuals, although there are certainly other possibilities, such as the use of lines dual to points. In many cases of exploratory analysis, however, the display of supplementary information attached to some or all of the points will be crucial for successful interpretation. The primary co-ordinate axes should, of course, be chosen to express the main dependenceexplicitly, if not initially certainly in the final presentation of conclusions." David R Cox,"Some Remarks on the Role in Statistics of Graphical Methods", Applied Statistics 27 (1), 1978)

"To see is to reason. Thus, the use of visual forms of communication has great potential for influencing what a person thinks. Graphic presentation is always much more than a way to present just facts or information. Rather, it is a way to influence thought, and, as such, graphics can be a powerful mode of persuasion." (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)

"Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers even a very large set - is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"The information on a plot should be relevant to the goals of the analysis. This means that in choosing graphical methods we should match the capabilities of the methods to our needs in the context of each application. [...] Scatter plots, with the views carefully selected as in draftsman's displays, casement displays, and multiwindow plots, are likely to be more informative. We must be careful, however, not to confuse what is relevant with what we expect or want to find. Often wholly unexpected phenomena constitute our most important findings." (John M Chambers et al, "Graphical Methods for Data Analysis", 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)

"When a graph is constructed, quantitative and categorical information is encoded, chiefly through position, size, symbols, and color. When a person looks at a graph, the information is visually decoded by the person's visual system. A graphical method is successful only if the decoding process is effective. No matter how clever and how technologically impressive the encoding, it is a failure if the decoding process is a failure. Informed decisions about how to encode data can be achieved only through an understanding of the visual decoding process, which is called graphical perception." (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)

"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)

"Using area to encode quantitative information is a poor graphical method. Effects that can be readily perceived in other visualizations are often lost in an encoding by area." (William S Cleveland, "Visualizing Data", 1993)

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

"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)

"The acquisition of information is a flow from noise to order - a process converting entropy to redundancy. During this process, the amount of information decreases but is compensated by constant re-coding. In the recoding the amount of information per unit increases by means of a new symbol which represents the total amount of the old. The maturing thus implies information condensation. Simultaneously, the redundance decreases, which render the information more difficult to interpret." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"Displaying numerical information always involves selection. The process of selection needs to be described so that the reader will not be misled." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Information needs representation. The idea that it is possible to communicate information in a 'pure' form is fiction. Successful risk communication requires intuitively clear representations. Playing with representations can help us not only to understand numbers" (describe phenomena) but also to draw conclusions from numbers" (make inferences). There is no single best representation, because what is needed always depends on the minds that are doing the communicating." (Gerd Gigerenzer, "Calculated Risks: How to know when numbers deceive you", 2002)

"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)

"[...] when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception‐based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"Dashboards aren't all that different from some of the other means of presenting information, but when properly designed the single-screen display of integrated and finely tuned data can deliver insight in an especially powerful way." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"Merely drawing a plot does not constitute visualization. Visualization is about conveying important information to the reader accurately. It should reveal information that is in the data and should not impose structure on the data." (Robert Gentleman, "Bioinformatics and Computational Biology Solutions using R and Bioconductor", 2005)

"One graph is more effective than another if its quantitative information can be decoded more quickly or more easily by most observers. […] This definition of effectiveness assumes that the reason we draw graphs is to communicate information - but there are actually many other reasons to draw graphs." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"Words, numbers, pictures, diagrams, graphics, charts, tables belong together. Excellent maps, which are the heart and soul of good practices in analytical graphics. routinely integrate words, numbers, line-art, grids, measurement scales. Rarely is a distinction among the different modes of evidence useful for making sound inferences. It is all information after all. Thus the Fourth Principle for the analysis and presentation of data: 'Completely integrate words, numbers, images, diagrams.'" (Edward R Tufte, "Beautiful Evidence", 2006)

"Data visualization [...] expresses the idea that it involves more than just representing data in a graphical form" (instead of using a table). The information behind the data should also be revealed in a good display; the graphic should aid readers or viewers in seeing the structure in the data. The term data visualization is related to the new field of information visualization. This includes visualization of all kinds of information, not just of data, and is closely associated with research by computer scientists." (Antony Unwin et al, "Introduction" [in "Handbook of Data Visualization"], 2008)

"Knowledge workers and BI experts must continually evaluate the reports, dashboards, alerts, and other mechanisms for disseminating factual information to ensure the design facilitates insight." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"One of the easiest ways to display data badly is to display as little information as possible. This includes not labelling axes and titles adequately, and not giving units. In addition, information that is displayed can be obscured by including unnecessary and distracting details." (Jenny Freeman et al, "How to Display Data", 2008)

"Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. [...] We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out picture is clear and accurate. But is it?" (Leonard Mlodinow, "The Drunkard’s Walk: How Randomness Rules Our Lives", 2008)

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

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

"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)

"When a chart is presented properly, information just lows to the viewer in the clearest and most efficient way. There are no extra layers of colors, no enhancements to distract us from the clarity of the information." (Dona Wong, "The Wall Street Journal guide to information graphics: The dos and don’ts of presenting data, facts, and figures", 2010)

"The fact that an information graphic is designed to help us complete certain intellectual tasks is what distinguishes it from fine art." (Alberto Cairo, "The Functional Art", 2011)

"Explanatory data visualization is about conveying information to a reader in a way that is based around a specific and focused narrative. It requires a designer-driven, editorial approach to synthesize the requirements of your target audience with the key insights and most important analytical dimensions you are wishing to convey." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"The main difference between journalistic and artistic infographics is that, while in the first information must try to be as objective as possible, the second supports a complete subjectivity and can lend itself to different interpretations, all of them valid. That’s the concept of 'subjective infographic', something apparently contradictory." (Jaime Serra, [interviewed] 2012)

"Diagrams furnish only approximate information. They do not add anything to the meaning of the data and, therefore, are not of much use to a statistician or research worker for further mathematical treatment or statistical analysis. On the other hand, graphs are more obvious, precise and accurate than the diagrams and are quite helpful to the statistician for the study of slopes, rates of change and estimation," (interpolation and extrapolation), wherever possible." (S C Gupta & Indra Gupta, "Business Statistics", 2013)

"A great infographic leads readers on a visual journey, telling them a story along the way. Powerful infographics are able to capture people’s attention in the first few seconds with a strong title and visual image, and then reel them in to digest the entire message. Infographics have become an effective way to speak for the creator, conveying information and image simultaneously." (Justin Beegel, "Infographics For Dummies", 2014)

"There is a story in your data. But your tools don’t know what that story is. That’s where it takes you - the analyst or communicator of the information - to bring that story visually and contextually to life." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

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

"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)

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

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

"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)

"Before even thinking about charts, it should be recognised that the table on its own is extremely useful. Its clear structure, with destination regions organised in columns and origins in rows, allows the reader to quickly look up any value - including totals - quickly and precisely. That’s what tables are good for. The deficiency of the table, however, is in identifying patterns within the data. Trying to understand the relationships between the numbers is difficult because, to compare the numbers with each other, the reader needs to store a lot of information in working memory, creating what psychologists refer to as a high 'cognitive load'." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"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)

"The narrative in the map offers a linear, spatial and rhizomatic organization. Readers can follow a temporal structure by following the sequence of numbers and dates, but they can also go to parts of the map that are most relevant to their own interests. Ultimately, the reader can follow a variety of narrative paths that at times follow linear time, contours of the land and water, or intensity of information." (Peter A Hall & Patricio Dávila, "Critical Visualization: Rethinking the Representation of Data", 2022)

"Your eyes and your brain always notice more dynamic visual information first and fastest. The implicit lesson is to make the idea you want people to see stand out. Conversely, make sure you’re not helping people see something that either doesn’t help convey your idea or actively fights against it." (Scott Berinato, "Good Charts : the HBR guide to making smarter, more persuasive data visualizations", 2023)

See also the quotes on "Information" in Data Science, Strategic Management 

📉Graphical Representation: Curvature (Just the Quotes)

"Characterizing a two-dimensional scatterplot is relatively easy, particularly with the full range of recently developed graphical enhancements at hand. However, standard patterns to watch for in three-dimensional plots are not as well understood as they are in many two-dimensional plots. We can certainly look for very general characteristics like curvature in three-dimensional plots, but it may not be clear how or if the curvature itself should be characterized. It is also possible to obtain useful insights into higher-dimensional scatterplots, but for the most part their interpretation must rely on lower-dimensional constructions. Similar statements apply to scatterplot matrices and various linked plots." (R Dennis Cook, "Regression Graphics: Ideas for Studying Regressions through Graphics", 1998)

"The fundamental concept of Gauss’s surface theory is the curvature, a quantity that is positive (and constant) for a sphere, zero for the plane and cylinder, and negative for surfaces that are 'saddle-shaped' in the neighborhood of each point." (John Stillwell, "The Four Pillars of Geometry", 2000) 

"Even though hyperbolic trees employ the same ranking principle as radial trees, based on a series of concentric circles, they do not operate in conventional Euclidean space, but instead within a spherical negative curvature based on hyperbolic geometry. Due to their magnifying feature, hyperbolic trees are useful for displaying and manipulating large hierarchies on a limited screen size. As visualizations ideally suited for direct manipulation, hyperbolic trees are rarely depicted in print and are found almost exclusively within the confines of their natural digital domain." (Manuel Lima, "The Book of Trees: Visualizing Branches of Knowledge", 2014)

"Once a model has been fitted to the data, the deviations from the model are the residuals. If the model is appropriate, then the residuals mimic the true errors. Examination of the residuals often provides clues about departures from the modeling assumptions. Lack of fit - if there is curvature in the residuals, plotted versus the fitted values, this suggests there may be whole regions where the model overestimates the data and other whole regions where the model underestimates the data. This would suggest that the current model is too simple relative to some better model." (DeWayne R Derryberry, "Basic data analysis for time series with R", 2014)

"A visual channel is a way to control the appearance of marks, independent of the dimensionality of the geometric primitive. […]  The motion-oriented channels include the motion pattern, for in stance, oscillating circles versus straight jumps, the direction of motion, and the velocity. Angle is also a channel, sometimes called tilt. Curvature is also a visual channel. Shape is a complex phenomenon, but it is treated as a channel in this framework." (Tamara Munzner, "Visualization: Analysis & Design", 2015) 

"Tensor fields typically contain a matrix at each cell in the field, capturing more complex structure than what can be expressed in a vector field. Tensor fields can measure properties such as stress, conductivity, curvature, and diffusivity." (Tamara Munzner, "Visualization: Analysis & Design", 2015) 

"The curvature channel is not very accurate, and it can only be used with line marks. It cannot be used with point marks that have no length, or area marks because their shape is fully constrained. The number of distinguishable bins for this channel is low, probably around two or three; it is in an equivalence class with volume (3D size) at the bottom of the magnitude channel ranking." (Tamara Munzner, "Visualization: Analysis & Design", 2015)

"Important features to look for in a scatter plot are whether there is one cloud of dots or several clusters, whether there is an upward or downward slope to the cloud of dots, and whether there is any curvature to the slope." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Adjusting scale is an important practice in data visualization. While the log transform is versatile, it doesn’t handle all situations where skew or curvature occurs. For example, at times the values are all roughly the same order of magnitude and the log transformation has little impact. Another transformation to consider is the square root transformation, which is often useful for count data." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)

📉Graphical Representation: Emphasis (Just the Quotes)

"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)

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

"Correct emphasis is basic to effective graphic presentation. Intensity of color is the simplest method of obtaining emphasis. For most reproduction purposes black ink on a white page is most generally used. Screens, dots and lines can, of course, be effectively used to give a gradation of tone from light grey to solid black. When original charts are the subjects of display presentation, use of colors is limited only by the subject and the emphasis desired." (Anna C Rogers, "Graphic Charts Handbook", 1961)

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

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

"[...] exploratory data analysis is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well as for those we believe might be there. Except for its emphasis on graphs, its tools are secondary to its purpose." (John W Tukey, [comment] 1979)

"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)

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

"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)

"[...] error bars are more effectively portrayed on dot charts than on bar charts. […] On the bar chart the upper values of the intervals stand out well, but the lower values are visually deemphasized and are not as well perceived as a result of being embedded in the bars. This deemphasis does not occur on the dot chart." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38 (4) 1984)

"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)

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

"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) 

"Arbitrary category sequence and misplaced pie chart emphasis lead to general confusion and weaken messages. Although this can be used for quite deliberate and targeted deceit, manipulation of the category axis only really comes into its own with techniques that bend the relationship between the data and the optics in a more calculated way. Many of these techniques are just twins of similar ruses on the value axis. but are none the less powerful for that." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"What distinguishes data tables from graphics is explicit comparison and the data selection that this requires. While a data table obviously also selects information, this selection is less focused than a chart's on a particular comparison. To the extent that some figures in a table are visually emphasised. say in colour or size and style of print. the table is well on its way to becoming a chart. If you're making no comparisons - because you have no particular message and so need no selection (in other words, if you are simply providing a database, number quarry or recycling facility) - tables are easier to use than charts." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"One way a chart can lie is through overemphasis of the size and scale of items, particularly when the dimension of depth isnʼt considered." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"As with dot plots, the scale on line charts has a lot to do with how the message is conveyed. For example, using too large a scale runs the risk that viewers may gloss over a very important story in the data. However, using too small a scale might lead you to overemphasize minor fluctuations. As with dot plots, designers should plot all of the data points so that the line chart takes up two-thirds of the y-axis’s total scale." (Jason Lankow et al, "Infographics: The power of visual storytelling", 2012)

"Color is a unique tool that you should use with care. Bold colors imply emphasis on a notable item, and when colors are used everywhere, it is difficult for people to determine where to direct their attention. When everything is highlighted, nothing is highlighted. Use this power to highlight sparingly on each slide, to point the viewer to the main thrust of your messaging." (Jason Lankow et al, "Infographics: The power of visual storytelling", 2012)

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

"Important data should be represented by graphical elements that are more visually distinct than those representing less important information." (Colin Ware, "Information Visualization: Perception for Design" 4th Ed., 2021)

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

📉Graphical Representation: Deception (Just the Quotes)

"The zero of the scale should appear on every chart, and should shown by a heavy line carried across the sheet. If this is not done the reader may assume the bottom of the sheet to be zero and so be misled. The scale should be graduated from zero to a little over the maximum figure to be plotted on the charts, so that there will be a space between the highest peak on the curve and the top of the chart." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Under certain conditions, however, the ordinary form of graphic chart is slightly misleading. It will be conceded that its true function is to portray comparative fluctuations. This result is practically secured when the factors or quantities compared are nearly of the same value or volume, but analysis will show that this is not accomplished when the amounts compared differ greatly in value or volume. [...] The same criticism applies to charts which employ or more scales for various curve. If the different scale are in proper proportion, the result is the same as with one scale, but when two or more scales are used which are not proportional an indication may be given with respect to comparative fluctuations which is absolutely false." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"When plotting any curve the vertical scale should, if possible, be chosen so that the zero of the scale will appear on the chart. Otherwise, the reader may assume the bottom of the chart to be zero and so be grossly misled. Zero should always be indicated by a broad line much wider than the ordinary co-ordinate lines used for the background of the chart." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"In short, the rule that no more dimensions or axes should be used in the chart than the data calls for, is fundamental. Violate this rule and you bring down upon your head a host of penalties. In the first place, you complicate your computing processes, or else achieve a grossly deceptive chart. If your chart becomes deceptive, it has defeated its purpose, which was to represent accurately. Unless, of course, you intended to deceive, in which case we are through with you and leave you to Mark Twain’s mercies. If you make your chart accurate, at the cost of considerable square or cube root calculating, you still have no hope, for the chart is not clear; your reader is more than likely to misunderstand it. Confusion, inaccuracy and deception always lie in wait for you down the path departing from the principle we have discussed - and one of them is sure to catch you." (Karl G Karsten, "Charts and Graphs", 1925)

"Note also, and this is important, that if through standing too close you should take a picture showing only the upper ends of the upright boards, but not their full lengths, you would consider the resulting picture not only a failure but actually deceptive. In other words, you must not omit the zero-line or base-line. While you would succeed in showing the variation of the top ends more clearly you would no longer have comparable lengths." (Karl G Karsten, "Charts and Graphs", 1925)

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

"[….] double-scale charts are likely to be misleading unless the two zero values coincide (either on or off the chart). To insure an accurate comparison of growth the scale intervals should be so chosen that both curves meet at some point. This treatment produces the effect of percentage relatives or simple index numbers with the point of juncture serving as the base point. The principal advantage of this form of presentation is that it is a short-cut method of comparing the relative change of two or more series without computation. It is especially useful for bringing together series that either vary widely in magnitude or are measured in different units and hence cannot be compared conveniently on a chart having only one absolute-amount scale. In general, the double scale treatment should not be used for presenting growth comparisons to the general reader." (Kenneth W Haemer, "Double Scales Are Dangerous", The American Statistician Vol. 2 (3) , 1948)

"An important rule in the drafting of curve charts is that the amount scale should begin at zero. In comparisons of size the omission of the zero base, unless clearly indicated, is likely to give a misleading impression of the relative values and trend." (Rufus R Lutz, "Graphic Presentation Simplified", 1949)

"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)

"Just like the spoken or written word, statistics and graphs can lie. They can lie by not telling the full story. They can lead to wrong conclusions by omitting some of the important facts. [...] Always look at statistics with a critical eye, and you will not be the victim of misleading information." (Dyno Lowenstein, "Graphs", 1976)

"Probably one of the most common misuses (intentional or otherwise) of a graph is the choice of the wrong scale - wrong, that is, from the standpoint of accurate representation of the facts. Even though not deliberate, selection of a scale that magnifies or reduces - even distorts - the appearance of a curve can mislead the viewer." (Peter H Selby, "Interpreting Graphs and Tables", 1976)

"For many people the first word that comes to mind when they think about statistical charts is 'lie'. No doubt some graphics do distort the underlying data, making it hard for the viewer to learn the truth. But data graphics are no different from words in this regard, for any means of communication can be used to deceive. There is no reason to believe that graphics are especially vulnerable to exploitation by liars; in fact, most of us have pretty good graphical lie detectors that help us see right through frauds." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"Graphs are used to meet the need to condense all the available information into a more usable quantity. The selection process of combining and condensing will inevitably produce a less than complete study and will lead the user in certain directions, producing a potential for misleading." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"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)

"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)

"The bar of a bar chart has two aspects that can be used to visually decode quantitative information-size (length and area) and the relative position of the end of the bar along the common scale. The changing sizes of the bars is an important and imposing visual factor; thus it is important that size encode something meaningful. The sizes of bars encode the magnitudes of deviations from the baseline. If the deviations have no important interpretation, the changing sizes are wasted energy and even have the potential to mislead." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38 (4) 1984) 

"The rule is that a graph of a change in a variable with time should always have a vertical scale that starts with zero. Otherwise, it is inherently misleading." (Douglas A Downing & Jeffrey Clark, "Forgotten Statistics: A Self-Teaching Refresher Course", 1996)

"Displaying numerical information always involves selection. The process of selection needs to be described so that the reader will not be misled." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"Averages, ranges, and histograms all obscure the time-order for the data. If the time-order for the data shows some sort of definite pattern, then the obscuring of this pattern by the use of averages, ranges, or histograms can mislead the user. Since all data occur in time, virtually all data will have a time-order. In some cases this time-order is the essential context which must be preserved in the presentation." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"[...] when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception‐based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading." (Colin Ware, "Information Visualization: Perception for Design" 2nd Ed., 2004)

"Comparing series visually can be misleading […]. Local variation is hidden when scaling the trends. We first need to make the series stationary (removing trend and/or seasonal components and/or differences in variability) and then compare changes over time. To do this, we log the series (to equalize variability) and difference each of them by subtracting last year’s value from this year’s value." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Arbitrary category sequence and misplaced pie chart emphasis lead to general confusion and weaken messages. Although this can be used for quite deliberate and targeted deceit, manipulation of the category axis only really comes into its own with techniques that bend the relationship between the data and the optics in a more calculated way. Many of these techniques are just twins of similar ruses on the value axis. but are none the less powerful for that." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"If you want to hide data, try putting it into a larger group and then use the average of the group for the chart. The basis of the deceit is the endearingly innocent assumption on the part of your readers that you have been scrupulous in using a representative average: one from which individual values do not deviate all that much. In scientific or statistical circles, where audiences tend to take less on trust, the 'quality' of the average (in terms of the scatter of the underlying individual figures) is described by the standard deviation, although this figure is itself an average." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"The donut, its spelling betrays its origins, is nearly always more deceit friendly than the pie, despite being modelled on a life-saving ring. This is because the hole destroys the second most important value- defining element, by hiding the slice angles in the middle." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"There are some chart types that occasionally appear in print but are so bad that they serve neither honesty nor deceit. Among these monuments to human ingenuity at the expense of common sense are the concentric donut and overlapping segments. The concentric donut is really just a bar or column chart bent back on itself to save space. However as anyone who has ever watched a two or four hundred metre race will know, to make sense of the order of arrival at the tape you have to stagger the start to take account of the bend in the track. Blithely ignoring this problem, the concentric donut uses to diminish the difference between the inner and the outer absolute values by anything up to 2.5 times." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"[…] a graph is nothing but a visual metaphor. To be truthful, it must correspond closely to the phenomena it depicts: longer bars or bigger pie slices must correspond to more, a rising line must correspond to an increasing amount. If a graphical depiction of data does not faithfully follow this principle, it is almost sure to be misleading. But the metaphoric attachment of a graphic goes farther than this. The character of the depiction ism a necessary and sufficient condition for the character of the data. When the data change, so too must their depiction; but when the depiction changes very little, we assume that the data, likewise, are relatively unchanging. If this convention is not followed, we are usually misled." (Howard Wainer, "Graphic Discovery: A trout in the milk and other visuals" 2nd, 2008)

"Good graphic design is not a panacea for bad copy, poor layout or misleading statistics. If any one of these facets are feebly executed it reflects poorly on the work overall, and this includes bad graphs and charts." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

"It is tempting to make charts more engaging by introducing fancy graphics or three dimensions so they leap of f the page, but doing so obscures the real data and misleads people, intentionally or not." (Brian Suda, "A Practical Guide to Designing with Data", 2010)

📉Graphical Representation: Orientation (Just the Quotes)

"The scales of any curve-chart should be so selected that the chart will not be exaggerated in either the horizontal or the vertical direction. It is possible to cause a visual exaggeration of data by carelessly or intentionally selecting a scale which unduly stretches the chart in either the horizontal or the vertical direction. Just as the English language can be used to exaggerate to the ear, so charts can exaggerate to the eye." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)

"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)

"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)

"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)

"Stacked bar graphs do not show data structure well. A trend in one of the stacked variables has to be deduced by scanning along the vertical bars. This becomes especially difficult when the categories do not move in the same direction." (Gerald van Belle, "Statistical Rules of Thumb", 2002)

"We make angle judgments when we read a pie chart, but we don't judge angles very well. These judgments are biased; we underestimate acute angles (angles less than 90°) and overestimate obtuse angles" (angles greater than 90°). Also, angles with horizontal bisectors" (when the line dividing the angle in two is horizontal) appear larger than angles with vertical bisectors." (Naomi B Robbins, "Creating More effective Graphs", 2005)

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

"The donut, its spelling betrays its origins, is nearly always more deceit friendly than the pie, despite being modelled on a life-saving ring. This is because the hole destroys the second most important value- defining element, by hiding the slice angles in the middle." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"Mosaic plots are defined recursively, i.e., each variable that is introduced in a mosaic plot is plotted conditioned on the groups already established in the plot. As with barcharts, the area of bars or tiles is proportional to the number of observations (or the sum of the observation weights of a class). The direction along which bars are divided by a newly introduced variable is usually alternating, starting with the x-direction." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)

"Communication is the primary goal of data visualization. Any element that hinders - rather than helps - the reader, then, needs to be changed or removed: labels and tags that are in the way, colors that confuse or simply add no value, uncomfortable scales or angles. Each element needs to serve a particular purpose toward the goal of communicating and explaining information. Efficiency matters, because if you’re wasting a viewer’s time or energy, they’re going to move on without receiving your message." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Correlation measures the degree to which two phenomena are related to one another. [...] Two variables are positively correlated if a change in one is associated with a change in the other in the same direction, such as the relationship between height and weight. [...] A correlation is negative if a positive change in one variable is associated with a negative change in the other, such as the relationship between exercise and weight." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"New information is constantly flowing in, and your brain is constantly integrating it into this statistical distribution that creates your next perception" (so in this sense 'reality' is just the product of your brain’s ever-evolving database of consequence). As such, your perception is subject to a statistical phenomenon known in probability theory as kurtosis. Kurtosis in essence means that things tend to become increasingly steep in their distribution [...] that is, skewed in one direction. This applies to ways of seeing everything from current events to ourselves as we lean 'skewedly' toward one interpretation, positive or negative. Things that are highly kurtotic, or skewed, are hard to shift away from. This is another way of saying that seeing differently isn’t just conceptually difficult - it’s statistically difficult." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"Skewed data means data that is shifted in one direction or the other. Skewness can cause machine learning models to underperform. Many machine learning models assume normally distributed data or data structures to follow the Gaussian structure. Any deviation from the assumed Gaussian structure, which is the popular bell curve, can affect model performance. A very effective area where we can apply feature engineering is by looking at the skewness of data and then correcting the skewness through normalization of the data." (Anthony So et al, "The Data Science Workshop" 2nd Ed., 2020)

"Sparklines focus on the trend over time and the direction rather than the actual values. Sparklines are used to visualize volatility or outliers. They are usually kept quite narrow on dashboards but still maintain an aspect ratio of 2:3." (Lorna Brown, "Tableau Desktop Cookbook", 2020)

"Another word of caution for dot plots that show changes over time. The dot plot is, by definition, a summary chart. It does not show all of the data in the intervening years. If the data between the two dots generally move in the same direction, a dot plot is sufficient. But if the data contain sharp variations year by year, a dot plot will obscure that pattern" (as it also does for bar charts)." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

📉Graphical Representation: Pictures (Just the Quotes)

"All of this information might be useful and even, for certain purposes, necessary. It is, so to speak, the statistical data of the question. But it yields no picture. A map or a globe gives us this mental picture almost in a flash. And that is precisely the use and service of a chart." (Carl Snyder, [in Karl G Karsten, "Charts and Graphs", 1925] 1923)

"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)

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

"The word data (singular: datum) refers to bits and pieces of information. such as numbers. symbols. words, pictures, gestures, or sounds. Data represent nonstructured information. In short, data are incoherent, whereas information is coherent." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers even a very large set - is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful." (Edward R Tufte, "The Visual Display of Quantitative Information", 1983)

"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)

"Words, numbers, pictures, diagrams, graphics, charts, tables belong together. Excellent maps, which are the heart and soul of good practices in analytical graphics. routinely integrate words, numbers, line-art, grids, measurement scales. Rarely is a distinction among the different modes of evidence useful for making sound inferences. It is all information after all. Thus the Fourth Principle for the analysis and presentation of data: 'Completely integrate words, numbers, images, diagrams.'" (Edward R Tufte, "Beautiful Evidence", 2006)

"Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. [...] We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out picture is clear and accurate. But is it?" (Leonard Mlodinow, "The Drunkard’s Walk: How Randomness Rules Our Lives", 2008) 

"The universal intelligibility of a pictogram is inversely proportional to its complexity and potential for interpretive ambiguity." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012)

"A great infographic leads readers on a visual journey, telling them a story along the way. Powerful infographics are able to capture people’s attention in the first few seconds with a strong title and visual image, and then reel them in to digest the entire message. Infographics have become an effective way to speak for the creator, conveying information and image simultaneously." (Justin Beegel, "Infographics For Dummies", 2014)

"Graphs can help us interpret data and draw inferences. They can help us see tendencies, patterns, trends, and relationships. A picture can be worth not only a thousand words, but a thousand numbers. However, a graph is essentially descriptive - a picture meant to tell a story. As with any story, bumblers may mangle the punch line and the dishonest may lie." (Gary Smith, "Standard Deviations", 2014)

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

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