✏️Roxy Peck - Collected Quotes

"A graphical display, when used appropriately, can be a powerful tool for organizing and summarizing data. By sacrificing some of the detail of a complete listing of a data set, important features of the data distribution are more easily seen and more easily communicated to others." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"A histogram for discrete numerical data is a graph of the frequency or relative frequency distribution, and it is similar to the bar chart for categorical data. Each frequency or relative frequency is represented by a rectangle centered over the corresponding value (or range of values) and the area of the rectangle is proportional to the corresponding frequency or relative frequency." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"A time-series plot (sometimes also called a time plot) is a simple graph of data collected over time that can be invaluable in identifying trends or patterns that might be of interest.A time-series plot can be constructed by thinking of the data set as a bivariate data set, where y is the variable observed and x is the time at which the observation was made. These (x, y) pairs are plotted as in a scatterplot. Consecutive observations are then connected by a line segment; this aids in spotting trends over time." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"A unimodal histogram that is not symmetric is said to be skewed. If the upper tail of the histogram stretches out much farther than the lower tail, then the distribution of values is positively skewed or right skewed. If, on the other hand, the lower tail is much longer than the upper tail, the histogram is negatively skewed or left skewed." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"A well-designed experiment requires more than just manipulating the explanatory variables; the design must also eliminate other possible explanations or the experimental results will not be conclusive." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Be careful not to confuse clustering and stratification. Even though both of these sampling strategies involve dividing the population into subgroups, both the way in which the subgroups are sampled and the optimal strategy for creating the subgroups are different. In stratified sampling, we sample from every stratum, whereas in cluster sampling, we include only selected whole clusters in the sample. Because of this difference, to increase the chance of obtaining a sample that is representative of the population, we want to create homogeneous groups for strata and heterogeneous (reflecting the variability in the population) groups for clusters." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Bias in sampling is the tendency for samples to differ from the corresponding population in some systematic way. Bias can result from the way in which the sample is selected or from the way in which information is obtained once the sample has been chosen. The most common types of bias encountered in sampling situations are selection bias, measurement or response bias, and nonresponse bias." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Descriptive statistics is the branch of statistics that includes methods for organizing and summarizing data. Inferential statistics is the branch of statistics that involves generalizing from a sample to the population from which the sample was selected and assessing the reliability of such generalizations." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Pie charts can be used effectively to summarize a single categorical data set if there are not too many different categories. However, pie charts are not usually the best tool if the goal is to compare groups on the basis of a categorical variable." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Populations with no variability are exceedingly rare, and they are of little statistical interest because they present no challenge! In fact, variability is almost universal. It is variability that makes life (and the life of a statistician, in particular) interesting. We need to understand variability to be able to collect, describe, analyze, and draw conclusions from data in a sensible way." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"[… ] statistics is about understanding the role that variability plays in drawing conclusions based on data. […] Statistics is not about numbers; it is about data - numbers in context. It is the context that makes a problem meaningful and something worth considering." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"Statistics is the scientific discipline that provides methods to help us make sense of data. Statistical methods, used intelligently, offer a set of powerful tools for gaining insight into the world around us." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"The goal of random sampling is to produce a sample that is likely to be representative of the population. Although random sampling does not guarantee that the sample will be representative, it does allow us to assess the risk of an unrepresentative sample. It is the ability to quantify this risk that will enable us to generalize with confidence from a random sample to the corresponding population." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

"The use of the density scale to construct the histogram ensures that the area of each rectangle in the histogram will be proportional to the corresponding relative frequency. The formula for density can also be used when class widths are equal. However, when the intervals are of equal width, the extra arithmetic required to obtain the densities is unnecessary." (Roxy Peck et al, "Introduction to Statistics and Data Analysis" 4th Ed., 2012)

✏️Robert L Harris - Collected Quotes

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

"Although in most cases the actual value designated by a bar is determined by the location of the end of the bar, many people associate the length or area of the bar with its value. As long as the scale is linear, starts at zero, is continuous, and the bars are the same width, this presents no problem. When any of these conditions are changed, the potential exists that the graph will be misinterpreted." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996)

"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) 

"As a general rule, the fewer the time intervals used in the averaging process, the more closely the moving average curve resembles the curve of the actual data. Conversely, the greater the number of intervals, the smoother the moving average curve. […] Moving average curves tend to have a delayed reaction to changes." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"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)

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

"Technically, there is no limit as to the number of data series that can be plotted on a single graph. Practically, if the number goes above three or four the graph becomes confusing." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

"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) 

"When approximations are all that are needed, stacked area graphs are usually adequate. When accuracy is desired, this type of graph is generally not used, particularly when the values fluctuate significantly and/or the slopes of the curves are steep." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996) 

✏️Kate Strachnyi - Collected Quotes

"As beautiful as data can be, it’s not an al fresco painting that should be open to interpretation from anyone who walks by its section of the museum. Make bold, smart color choices that leave no doubt what the purpose of the data is." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Blue is a nice color for a lot of things, but it’s tough for people to tell the difference between shades of blue in a report. Light blue and dark blue and royal blue and navy blue have a tendency to run together, so differing shades are not going to make that big of a difference for audience members trying to unspool what’s being presented. The same goes for other colors: it’s not that easy for humans to tell the difference between varying shades of the same color (unless they are drastic)." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Colors and numbers are much more similar than we think. Using contrasting colors on different forms of information allows your audience to make a very clear delineation between the two, even when the setup and style are completely the same." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Color is by far the most abused and neglected tool in data visualization. We abuse it by making color choices that make no sense, and we neglect it when we populate our hard work with software default settings, which are a good place to start but can be customized to suit your needs. [...] Color - if used prudently - makes our visualizations more digestible and more informative." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data becomes more useful once it’s transformed into a data visualization or used in a data story. Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context and inspire action from your audience. Color can be very helpful when you are trying to make information stand out within your data visualizations." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data storytelling is a method of communicating information that is custom-fit for a specific audience and offers a compelling narrative to prove a point, highlight a trend, make a sale, or all of the above. [...] Data storytelling combines three critical components, storytelling, data science, and visualizations, to create not just a colorful chart or graph, but a work of art that carries forth a narrative complete with a beginning, middle, and end." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data visualization is the practice of taking insights found in data analysis and turning them into numbers, graphs, charts, and other visual concepts to make them easier to grasp, understand, learn from, and utilize.[...] The visualization of data can be thought of as both a science and an art in that the way it is displayed is often as important to its understanding as the actual information that is being displayed." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Good data stories have three key components: data, narrative, and visuals. [...] The data part is fairly obvious - data has to be accurate for the correct insights to be achieved. The narrative has to give a voice to the data in simple language, turning each data point into a character in the story with its own tale to tell. The visuals are what we are most concerned about. They have to allow us to be able to find trends and patterns in our datasets and do so easily and specifically. The last thing we want is for the most important points to be buried in rows and columns." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"One tip to keep an audience focused on your story without overwhelming them is to reduce the saturation of the colors [...] When you lower the brightness and intensity, you are reducing the cognitive load that your audience has to bear. [...] Regardless of what combinations you decide on, you need to avoid pure colors that are bright and saturated." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Our machines are helpers, not decision makers. Their insights are not the final word in the discussion, merely the work of our most nimble observers who can ramp up time spent on analysis by factors that our counterparts even a generation ago would have a hard time believing." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Sometimes, adding a divider to a visualization can help transform it from something that’s difficult to understand into a more effective visual." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"The lack of focus and commitment to color is a perplexing thing. When used correctly, color has no equal as a visualization tool - in advertising, in branding, in getting the message across to any audience you seek. Data analysts can make numbers dance and sing on command, but they sometimes struggle to create visually stimulating environments that convince the intended audience to tap their feet in time." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"The practice of finding relationships between different sets of data - also known as correlations - is the bread and butter of what data analysis, and by proxy data visualization, is all about." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Visualizations can remove the background noise from enormous sets of data so that only the most important points stand out to the intended audience. This is particularly important in the era of big data. The more data there is, the more chance for noise and outliers to interfere with the core concepts of the data set." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"When the colors are dull and neutral, they can communicate a sense of uniformity and an aura of calmness. Grays do a great job of mapping out the context of your story so that the more sharp colors highlight what you’re trying to explain. The power of gray comes in handy for all of our supporting details such as the axis, gridlines, and nonessential data that is included for comparative purposes. By using gray as the primary color in a visualization, we automatically draw our viewers’ eyes to whatever isn’t gray. That way, if we are interested in telling a story about one data point, we can do so quite easily."  (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

✏️Anna C Rogers - Collected Quotes

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

"Although flow charts are not used to portray or interpret statistical data, they possess definite utility for certain kinds of research and administrative problems. With a well-designed flow chart it is possible to present a large number of facts and relationships simply, clearly, and accurately, without resorting to extensive or involved verbal description." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"Circles of different size, however cannot properly be used to compare the size of different totals. This is because the reader does not know whether to compare the diameters or the areas (which vary as the squares of the diameters), and is likely to misjudge the comparison in either ease. Usually the circles are drawn so that their diameters are in correct proportion to each other; but then the area comparison is exaggerated. Component bars should be used to show totals of different size since their one dimension lengths can be easily judged not only for the totals themselves but for the component parts as well. Circles, therefore, can show proportions properly by variations in angles of sectors but not by variations in diameters."  (Anna C Rogers, "Graphic Charts Handbook", 1961)

"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)

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

"Many people use statistics as a drunkard uses a street lamp - for support rather than illumination. It is not enough to avoid outright falsehood; one must be on the alert to detect possible distortion of truth. One can hardly pick up a newspaper without seeing some sensational headline based on scanty or doubtful data." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"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)

"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)

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

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

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

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

"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)

✏️Peter H Selby - Collected Quotes

"A graph presents a limited number of figures in a bold and forceful manner. To do this it usually must omit a large number of figures available on the subject. The choice of what graphic format to use is largely a matter of deciding what figures have the greatest significance to the intended reader and what figures he can best afford to skip." (Peter H Selby, "Interpreting Graphs and Tables", 1976)

"A statistical table is a systematic arrangement of numerical data in columns and rows. Its purpose is to show quantitative facts clearly, concisely, and effectively. It should facilitate an understanding of the logical relationships among the numbers presented. Tables are used in the compilation of raw data, in the summarizing and analytic processes, and in the presentation of statistics in final form. A good table is the product of careful thinking and hard work. It is not just a package of figures put into neat compartments and ruled to make it look more attractive. It contains carefully selected data put together with thought and ingenuity to serve a specific purpose." (Peter H Selby, "Interpreting Graphs and Tables", 1976)

"Pie charts are awkward to label and do not fit as well on a report page as bar comparisons (vertical or horizontal). Thus a series of pies is less effective than a series of subdivided bars (or columns) for comparing a group of subdivided totals. Several pies require much more space than several bars. Moreover, the comparable components often are in a different location in each pie and so are hard to compare." (Peter H Selby, "Interpreting Graphs and Tables", 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)

"Remember, the primary function of a graph of any kind is to illustrate the relationship between two variables. [...] To draw any graph we must have established some relationship between the two variables. This relationship can be in the form of a formula (equation is the more mathematical term), as we have just seen, or simply a set of observations, as is common in all types of statistical work. Sometimes we develop set of observations and then try to find an equation that expresses, in mathematical language, the relationship between the two variables." (Peter H Selby, "Interpreting Graphs and Tables", 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)

"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)

✏️Bruce Robertson - Collected Quotes

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

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

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

"Good graphics can be spoiled by bad annotation. Labels must always be subservient to the information to be conveyed, and legibility should never be sacrificed for style. All the information on the sheet should be easy to read, and more important, easy to interpret. The priorities of the information should be clearly expressed by the use of differing sizes, weights and character of letters." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Maps containing marks that indicate a variety of features at specific locations are easy to produce and often revealing for the reader. You can use dots, numbers, and shapes, with or without keys. The basic map must always be simple and devoid of unnecessary detail. There should be no ambiguity about what happens where." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

"Maps used as charts do not need fine cartographic detail. Their purpose is to express ideas, explain relationships, or store data for consultation. Keep your maps simple. Edit out irrelevant detail. Without distortion, try to present the facts as the main feature of your map, which should serve only as a springboard for the idea you're trying to put across." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

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

"Wherever information has to be presented, charts offer an alternative to text and tables of figures. They are concise, memorable often intelligible without language, and can make significant additions to the story." (Bruce Robertson, "How to Draw Charts & Diagrams", 1988)

✏️Calvin F Schmid - Collected Quotes

"Although the pie or sector chart ranks very high in popular appeal, it is held in rather low esteem by many specialists in graphic presentation. Since the pie chart possesses more weaknesses perhaps than most graphic forms, it is especially important to observe proper discretion in its construction and application. The pie chart is used to portray component relations. The various sectors of a circle represent component parts of an aggregate or total." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"An organization chart portrays every essential part of an organization in its proper relation to all other parts. More specifically, it shows the relation of one official or department or function to another; titles and sometimes names of officials, and names of departments and their functions; and sources, lines, and types of authority." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954) 

"As a general rule it is recommended that the bar chart be used for simple comparison, particularly if there are more than four or five categories." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"Charts and graphs represent an extremely useful and flexible medium for explaining, interpreting, and analyzing numerical facts largely by means of points, lines, areas, and other geometric forms and symbols. They make possible the presentation of quantitative data in a simple, clear, and effective manner and facilitate comparison of values, trends, and relationships. Moreover, charts and graphs possess certain qualities and values lacking in textual and tabular forms of presentation." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

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

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

"The number of grid lines should be kept to a minimum. This means that there should be just enough coordinate lines in the field so that the eye can readily interpret the values at any point on the curve. No definite rule can be specified as to the optimum number of lines in a grid. This must be left to the discretion of the chart-maker and can come only from experience. The size of the chart, the type and range of the data, the number of curves, the length and detail of the period covered, as well as other factors, will help to determine the number of grid lines." (Calvin F Schmid, "Handbook of Graphic Presentation", 1954)

"The trilinear chart is used to portray simultaneously three variables expressed in the form of elements or components of a total. It is characteristically a one-hundred percent chart, since the sum of the three values indicated is equal to 100 percent. The trilinear chart is drawn in the form of an equilateral triangle, each side of which is calibrated in equal percentage divisions ranging from zero to 100. The rulings are projected across the chart parallel to the sides in the manner of coordinates." (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)

✏️Alan Graham - Collected Quotes

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

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

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

"Cleverly drawn pictures can sometimes disguise or render invisible what is there. At other times, they can make you see things that are not really there. It is helpful to be aware of how these illusions are achieved, as some of the illusionist’s 'tricks of the trade' can also be found in distortions used in graphs and diagrams." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Exploratory Data Analysis is more than just a collection of data-analysis techniques; it provides a philosophy of how to dissect a data set. It stresses the power of visualisation and aspects such as what to look for, how to look for it and how to interpret the information it contains. Most EDA techniques are graphical in nature, because the main aim of EDA is to explore data in an open-minded way. Using graphics, rather than calculations, keeps open possibilities of spotting interesting patterns or anomalies that would not be apparent with a calculation (where assumptions and decisions about the nature of the data tend to be made in advance)." (Alan Graham, "Developing Thinking in Statistics", 2006) 

"People sometimes appeal to the 'law of averages' to justify their faith in the gambler’s fallacy. They may reason that, since all outcomes are equally likely, in the long run they will come out roughly equal in frequency. However, the next throw is very much in the short run and the coin, die or roulette wheel has no memory of what went before." (Alan Graham, "Developing Thinking in Statistics", 2006)

"People tend to give greater weight to the data that they have just been exposed to than other relevant data. […] This phenomenon, where people give greater attention to recent or easily available data, is often referred to as an availability error." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Probability is about making decisions under uncertainty - indeed, where there is no uncertainty, no decision is required, as you would simply choose the outcome that you know will occur. A 'good' or 'rational' decision favours the Cartesian principle that ‘when it is not in our power to follow what is true, we ought to follow what is most probable’. Of course, rational decisions sometimes turn out to be wrong. That does not mean that the decisions were bad - they may have been the best choices, given the information available at the time. […] In the long run, the vagaries of chance tend to even out, but in particular cases it can happen that the long shot comes in first. This is the corollary of a 'good' decision that has bad consequences - a 'bad' or 'irrational' decision that turns out to be right." (Alan Graham, "Developing Thinking in Statistics", 2006) 

"Random number generators do not always need to be symmetrical. This misconception of assuming equal likelihood for each outcome is fostered in a restricted learning environment, where learners see only such situations (that is, dice, coins and spinners). It is therefore very important for learners to be aware of situations where the different outcomes are not equally likely (as with the drawing-pins example)." (Alan Graham, "Developing Thinking in Statistics", 2006)

"'Regression to the mean' describes a natural phenomenon whereby, after a short period of success, things tend to return to normal immediately afterwards. This notion applies particularly to random events." (Alan Graham, "Developing Thinking in Statistics", 2006)

"The notion of outcomes covering a space is a very useful mental image, as it ties in strongly with the use of Venn diagrams and tables for clarifying the nature of possible events resulting from a trial. There are two important aspects to this. First, when enumerating the various outcomes that comprise an event, the number of (equally. likely) outcomes should correspond, visually, with the area of that part of the diagram represented by the event in question - the greater the probability, the larger the area. Secondly, where events overlap (for example, when rolling a die, consider the two events 'getting an even score' and 'getting a score greater than 2' ), the various regions in the Venn diagram help to clarify the various combinations of events that might occur." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Unlike in mathematics, where relationships tend to be clearly defined and unambiguous, statistical relationships tend to reflect the general messiness of the real world from which the data were drawn." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Use of a histogram should be strictly reserved for continuous numerical data or for data that can be effectively modelled as continuous […]. Unlike bar charts, therefore, the bars of a histogram corresponding to adjacent intervals should not have gaps between them, for obvious reasons." (Alan Graham, "Developing Thinking in Statistics", 2006)

"What sets statistics apart from the rest of mathematics is that in statistics events occur under conditions of uncertainty. Whereas in pure mathematics all even numbers possess the property of evenness, a statistical variable may take a range of different values that are usually unpredictable in advance." (Alan Graham, "Developing Thinking in Statistics", 2006)

"When it comes to drawing a picture of continuous data, you need to think through carefully where one interval ends and the next one begins. Failing to do this can result in overlaps or gaps between adjacent intervals, which can cause confusion." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Where correlation exists, it is tempting to assume that one of the factors has caused the changes in the other (that is, that there is a cause-and-effect relationship between them). Although this may be true, often it is not. When an unwarranted or incorrect assumption is made about cause and effect, this is referred to as spurious correlation […]" (Alan Graham, "Developing Thinking in Statistics", 2006)

"Whereas regression is about attempting to specify the underlying relationship that summarises a set of paired data, correlation is about assessing the strength of that relationship. Where there is a very close match between the scatter of points and the regression line, correlation is said to be 'strong' or 'high' . Where the points are widely scattered, the correlation is said to be 'weak' or 'low'." (Alan Graham, "Developing Thinking in Statistics", 2006)

✏️Linda Reynolds - Collected Quotes

"As a general rule, headings should not be centred. The eyes tend to move automatically to the left hand margin at the end of each line, and centred headings are therefore likely to interrupt the smooth flow of reading. They may even be missed altogether." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"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 the case of graphs, the number of lines which can be included on any one illustration will depend largely on how close the lines are and how often they cross one another. Three or four is likely to be the maximum acceptable number. In some instances, there may be an argument for using several graphs with one line each as opposed to one graph with multiple lines. It has been shown that these two arrangements are equally satisfactory if the user wishes to read off the value of specific points; if, however, he wishes to compare the lines, than the single multi-line graph is superior." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"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)

"The basic principle which should be observed in designing tables is that of grouping related data, either by the use of space or, if necessary, rules. Items which are close together will be seen as being more closely related than items which are farther apart, and the judicious use of space is therefore vitally important. Similarly, ruled lines can be used to relate and divide information, and it is important to be sure which function is required. Rules should not be used to create closed compartments; this is time-wasting and it interferes with scanning." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"The ease and speed with which tables can be understood depends very much on the tabulation logic. The author must ask himself what information the reader already has when he consults a particular table, and what information he is seeking from it. The row and column headings should relate to the information he already has, thus leading him to the information he seeks which is displayed in the body of the table." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

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

"The frequency of labelled scale calibrations on the axes of a graph can significantly affect the accuracy with which it is interpreted. As little interpolation as possible should be required of the user, in order to minimise errors. If single units cannot be marked, it has been suggested that multiples of 2,5 or 10 should be used." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"The space between columns, on the other hand, should be just sufficient to separate them clearly, but no more. The columns should not, under any circumstances, be spread out merely to fill the width of the type area. […] Sometimes, however, it is difficult to avoid undesirably large gaps between columns, particularly where the data within any given column vary considerably in length. This problem can sometimes be solved by reversing the order of the columns […]. In other instances the insertion of additional space after every fifth entry or row can be helpful, […] but care must be taken not to imply that the grouping has any special meaning." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 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)

"The practice of framing an illustration with a drawn rectangle is not recommended. This kind of typographic detailing should never be added purely for aesthetic reasons or for decoration. A simple, purely functional drawing will automatically be aesthetically pleasing. Unnecessary lines usually reduce both legibility and attractiveness." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"Wherever possible, numerical tables should be explicit rather than implicit, i.e. the information should be given in full. In an implicit table, the reader may be required to add together two values in order to obtain a third which is not explicitly stated in the table. […] Implicit tables save space, but require more effort on the part of the reader and may cause confusion and errors. They are particularly unsuitable for slides and other transient displays." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

✏️Forrest W Young - Collected Quotes

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

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

"A statistical hypothesis is a statement that specifies a set of possible distributions of the data variable x. In hypothesis testing, the goal is to see if there is sufficient statistical evidence to reject a presumed null hypothesis in favor of a conjectured alternative hypothesis. Confirmatory statistics used the formalisms of mathematical proofs, theorems, derivations, and so on, to provide a firm mathematical foundation for hypothesis testing."(Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"After all, we do agree that statistical data analysis is concerned with generating and evaluating hypotheses about data. For us, generating hypotheses means that we are searching for patterns in the data - trying to 'see what the data seem to say'. And evaluating hypotheses means that we are seeking an explanation or at least a simple description of what we find - trying to verify what we believe we see." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"Commonly, data do not make a clear and unambiguous statement about our world, often requiring tools and methods to provide such clarity. These methods, called statistical data analysis, involve collecting, manipulating, analyzing, interpreting, and presenting data in a form that can be used, understood, and communicated to others." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

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

"Histograms and frequency polygons display a schematic of a numeric variable's frequency distribution. These plots can show us the center and spread of a distribution, can be used to judge the skewness, kurtosis, and modicity of a distribution, can be used to search for outliers, and can help us make decisions about the symmetry and normality of a distribution." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"Linking is a powerful dynamic interactive graphics technique that can help us better understand high-dimensional data. This technique works in the following way: When several plots are linked, selecting an observation's point in a plot will do more than highlight the observation in the plot we are interacting with - it will also highlight points in other plots with which it is linked, giving us a more complete idea of its value across all the variables. Selecting is done interactively with a pointing device. The point selected, and corresponding points in the other linked plots, are highlighted simultaneously. Thus, we can select a cluster of points in one plot and see if it corresponds to a cluster in any other plot, enabling us to investigate the high-dimensional shape and density of the cluster of points, and permitting us to investigate the structure of the disease space." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"One problem for visualizing multiple views is that of laying out the plots. Indeed, there are some plots, such as scatterplot matrixes and trellis displays, that are formed just by arranging simpler plots according to certain rules. Scatterplot matrices, for example, arrange scatterplots side by side so that each variable in a dataset is graphed against the other variables, with the graphs being displayed as a row or a column of the matrix. This lets the user rapidly inspect all of the bivariate relationships among the variables, permitting the detection of outliers, nonlinearities, and other features of the data." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

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

"The way that the model differs from the data gives us clues about how we can improve our model. We can use mosaic displays to find the specific ways in which the model is different from the data, since mosaics show the residuals (or differences) of the cells with respect to the model. Looking at these differences, we can observe patterns in the deviation that will help us in our search." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

"Transforming data to measurements of a different kind can clarify and simplify hypotheses that have already been generated and can reveal patterns that would otherwise be hidden." (Forrest W Young et al, "Visual Statistics: Seeing data with dynamic interactive graphics", 2016)

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

✏️Vidya Setlur - Collected Quotes

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

"A well-designed dashboard needs to provide a similar experience; information cannot be placed just anywhere on the dashboard. Charts that relate to one another are usually positioned close to one another. Important charts often appear larger and more visually prominent than less important ones. In other words, there are natural sizes for how a dashboard comprises charts based on the task and context." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

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

"Annotations are in-chart clarifiers. They identify salient points within the visualization using placement as a primary attribute in their understanding. They call out peaks, averages, or notable reference points." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"As we enter into certain types of analytical conversations, we expect the conversations to flow in a predictable and cohesive manner. A KPI dashboard, for example, uses redundant structures across specific dimensions or measures to convey information. A dashboard with a top-down exposition style provides high-level information first and clarifies downward, while a bottom-up dashboard starts with the details and clarifies them against the larger picture." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

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

"Beyond the design of individual charts, the sequence of data visualizations creates grammar within the exposition. Cohesive visualizations follow common narrative structures to fully express their message. Order matters."  (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Conversational repair is the process people use to detect and resolve problems in communicating, receiving, and understanding. Through repair, participants in social interaction display how they establish and maintain communication and mutual understanding. Language interpretation formalizes multiple levels of repair, from monitoring and evaluating various benchmarks of accuracy to proper ways to intervene and seek clarification." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 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)

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

"Cohesion means ideas work together to build a unified whole, which helps conversation interlink in purposeful ways, and the basic parts adhere to grammar." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Coloring needs to be semantically relevant and is also defined by the context." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

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

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

"Data that is well prepared makes the analysis easier and allows a deeper exploration of patterns. It helps the analyst sift through the data with less friction. Data that is well crafted holds up to rigorous analysis and presentation. It removes the wall between us and the data and allows us to see the patterns. Well-shaped data isn't only functional, it's also aesthetic." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Design choices include more deliberate thought put into resizing, cropping, simplifying, and enhancing information within the limited real estate. These thumbnails need to be visually interpretable, yet inviting and engaging to the audience." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Functionally aesthetic charts take categories, place, time, and numbers, weaving patterns and stories in creative ways. Data often loses precision when interacting in the real world." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"In practice, selecting charts may include effectiveness, user comfort, surrounding charts, text, software complexities of making the chart, how the data fits the chart, and what to expect if the chart continues to update on its own. Practitioners may choose a less-effective chart for a variety of reasons or may spread a task across several charts."(Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Knowing the semantics of your data helps with sensible data transformations." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Like multimodal reading, data literacy relies on both primary literacy skills and numeracy skills to truly make sense of the third layer: reading and understanding graphs. Charts codify numbers visually into parameters, using stylized marks to embed additional layers of meaning and space to provide quantitative relationships. Beyond the individual chart, data visualizations create ensembles of charts." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Maps are a type of chart that can convey relationships about space and relationships between objects that we relate to in the real world. Their effectiveness as a communication medium is strongly influenced by a host of factors: the nature of spatial data, the form and structure of representation, their intended purpose, the experience of the audience, and the context in the time and space in which the map is viewed. In other words, maps are a ubiquitous representation of spatial information that we can understand and relate to." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Positive and negative space help create balance, but they also draw interest." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

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

"Sequencing is relevant to all visualization (not just instructions) because the author can use graphics and conventions to sequence the reading of visualizations. Annotations, in particular, can be used very effectively to teach conventions and to influence sequencing." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Text should be treated as a first-class citizen, just like any chart type. The thoughtful placement of text along with its encodings of shape and color determine the visualization's layout, structure, and flow." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"The effective depiction of an icon often depends on how semantically resonant the image is to the information it represents. The use of icons in charts depends on various factors, including task, how representative they are of the underlying data, and their general recognizability." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"The rise of graphicacy and broader data literacy intersects with the technology that makes it possible and the critical need to understand information in ways current literacies fail. Like reading and writing, data literacy must become mainstream to fully democratize information access." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"The sizes of charts in space reflect how we convey information to a reader. In a dashboard context, the content, size, and space that the various charts occupy should reflect the form and function of the main message. As you saw with the bento box metaphor from the introduction, there needs to be deliberate thought put into the placement and size of each individual chart so that they all work together in harmony." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

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

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

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

"Using symbols is one common way of applying semantics to help make sense of the world. Symbols provide clues to understanding experiences by conveying recognizable meanings that are shared by societies." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

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

"When dealing with meaningful visual representation, aspects of a representation's meaning can be altered by modifying its visual characteristics; these characteristics are extensively explored in semiotics, the study of signs and symbols and their use or interpretation." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"We define analytical intent to be the goal that a consumer or analyst focuses on when performing either targeted or more open-ended data exploration and discovery. Analytical intent is expressed as part of a conversation between the user and a visualization interface." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"When integrating written text with charts in a functionally aesthetic way, the reader should be able to find the key takeaways from the chart or dashboard, taking into account the context, constraints, and reading objectives of the overall message."  (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

✏️Jenny Freeman - Collected Quotes

"Colour can be used to highlight text within a slide but care should be taken to not get carried away with lots of different colours. No more than three colours should be used on a single slide. It is important to consider the combination of colours to be used, as some colours work well together whilst others do not." (Jenny Freeman et al, "How to Display Data", 2008)

"Generally pie charts are to be avoided, as they can be difficult to interpret particularly when the number of categories is greater than five. Small proportions can be very hard to discern […] In addition, unless the percentages in each of the individual categories are given as numbers it can be much more difficult to estimate them from a pie chart than from a bar chart […]." (Jenny Freeman et al, "How to Display Data", 2008)

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

"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)

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

"Well-displayed data can clearly illuminate and enhance the interpretation of a study, while badly laid out data and results can obscure the message or at worst seriously mislead." (Jenny Freeman et al, "How to Display Data", 2008)

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

"Where there is no natural ordering to the categories it can be helpful to order them by size, as this can help you to pick out any patterns or compare the relative frequencies across groups. As it can be difficult to discern immediately the numbers represented in each of the categories it is good practice to include the number of observations on which the chart is based, together with the percentages in each category." (Jenny Freeman et al, "How to Display Data", 2008)

✏️Jonathan Schwabish - Collected Quotes

"Active titles don't make us biased, but descriptive titles do waste an opportunity to make a clear, compelling case. Of course, short, active titles aren't always possible - you may be making more than one point or your sole goal is to simply describe the data. Generally speaking, however, integrating your graphs as part of your argument creates a more cohesive approach to making your argument and telling your story." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"An outlier is a data point that is far away from other observations in your data. It may be due to random variability in the data, measurement error, or an actual anomaly. Outliers are both an opportunity and a warning. They potentially give you something very interesting to talk about, or they may signal that something is wrong in the data." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"Another cardinal sin of data visualization is what is called 'breaking the bar' - that is, using a squiggly line or shape to show that you've cropped one or more of the bars. It's tempting to do this when you have an outlier, but it distorts the relative values between the bars." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"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)

"As data communicators, it is therefore our responsibility to treat our work and our data as carefully and objectively as possible. It is also our responsibility to recognize where our data may suffer from underlying bias or error, or even implicit bias that data creators may themselves not even be aware of." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"As data visualization creators, we must understand our audience and know when a different graph can engage readers - and help them expand their own graphic literacy." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"Clutter is the main issue to keep in mind when assessing whether a paired bar chart is the right approach. With too many bars, and especially when there are more than two bars for each category, it can be difficult for the reader to see the patterns and determine whether the most important comparison is between or within the different categories." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"Data visualization is a mix of science and art. Sometimes we want to be closer to the science side of the spectrum - in other words, use visualizations that allow readers to more accurately perceive the absolute values of data and make comparisons. Other times we may want to be closer to the art side of the spectrum and create visuals that engage and excite the reader, even if they do not permit the most accurate comparisons." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"People see bar charts and line charts and pie charts all the time, and those charts are often boring. Boring graphs are forgettable. Different shapes and uncommon forms that move beyond the borders of our typical data visualization experience can draw readers in." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"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)

"Start with gray. Whenever you make a graph, start with all-gray data elements. By doing so, you force yourself to be purposeful and strategic in your use of color, labels, and other elements." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"Standard graphs, like bar and line charts, are so common because they are perceptually more accurate, familiar to people, and easy to create. Nonstandard graphs - those that use circles or curves, for instance - may not allow the reader to most accurately perceive the exact data values. But perceptual accuracy is not always the goal. And sometimes it's not a goal at all. Spurring readers to engage with a graph is sometimes just as important. Sometimes, it's more important. And nonstandard chart types may do just that. In some cases, nonstandard graphs may help show underlying patterns and trends in better ways that standard graphs. In other cases, the fact that these nonstandard graphs are different may make them more engaging, which we may sometimes need to first attract attention to the visualization."  (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

"The radial bar chart, also called the polar bar chart, arranges the bars to radiate outward from the center of a circle. This graph lies lowers on the perceptual ranking list because it is harder to compare the heights of the bars arranged around a circle than when they are arranged along a single flat axis. But this layout does allow you to fit more values in a compact space, and makes the radial bar chart well-suited for showing more data, frequent changes (such as monthly or daily), or changes over a long period of time." (Jonathan Schwabish, "Better Data Visualizations: A guide for scholars, researchers, and wonks", 2021)

✏️Andy Kriebel - Collected Quotes

"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)

"Calculating the percent change between two percentages is not completely inaccurate, but it can be very misleading. Instead, you should use the absolute change when you are working with percentages and want to show the difference between two points in time." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Data analysis is more than crunching numbers; it is about finding insights, identifying the unknown unknowns, and presenting the data in a simple yet deep enough way so that your audience can understand your insights and make decisions." (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)

"Ideally, the charts are designed in a way that gives your audience clarity and lets them understand the key insights very quickly. Color choices, highlighting, annotations, and other ways of drawing attention to your findings help in the process. By leaving white or blank space around your charts, you are able to keep the focus of your audience on the key message rather than distracting or confusing them." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Plotting numbers on a chart does not make you a data analyst. Knowing and understanding your data before you communicate it to your audience does."  (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Ranks do not explain how much one item varies from another. Ranked data is ordinal; that is, the data is categorical and has a sequence (e.g., who finished the race first, second, and third). That’s it! Ranked data can be used for showing the order of the data points. […] When working with ranked data, you cannot make inferences about the variance in the data; all you can say with certainty is which item is ranked higher than the others, not how much higher." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Simplicity for data visualization often focuses on minimizing the number of elements that do not add value to your display. These include borders, gridlines, axes lines, and boxes, which can easily distract from your core message. This recommendation also relates to the information itself. You should strive to create a visualization that focuses on specific aspects of the data, rather than including all fields and metrics but not saying much about any of them." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Simplicity in design can be recognized in visualizations that are clear, easy to understand, uncluttered, and impactful. Nonessential items are removed from these visualizations so that the data stands out, giving it space and removing distractions. Simplicity in design pays careful attention to the overall layout and positioning of individual components, the balance of charts and text elements, and the choice of colors, fonts, and icons, as well as the clarity with which all of these elements communicate to the audience." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Smoothing is a technique that can be used to remove some of the variation in short-term data in favor of emphasizing long-term trends." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018) 

"Taking an average of an average (the original percentage) does not result in a weighted average, which takes into account the sample size […]." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

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

"To become a great data analyst, you must be able to identify and deal with incomplete data and work to identify the data quality and accuracy issues in a data set." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Using a question as a title is a great way to guide the audience. The question helps you ensure that your charts respond directly to the question and when they do not, you can remove them. And that is the main point: You need to answer the question. If the data is not conclusive, say so. Give an explanation that relates back to your title and close the loop so that your audience is informed and gets the complete picture included in your analysis." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"Visually plotting time series data against a point in time reveals patterns relative to that period, thus allowing the reader to understand growth and decline before and after the given point in time." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

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

"When you are exploring your data, look for alternate views of the data; you just may find a more interesting insight."  (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

✏️Nicholas Strange - Collected Quotes

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

"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)

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

"Deceit through cumulation is a bit of a golden Oldie, but still frequently used in some highly respectable publications." (Nicholas Strange, "Smoke and Mirrors: How to bend facts and figures to your advantage", 2007)

"If you're really desperate to find some sort of correlation to add respectability to an otherwise unimpressive train of thought, you can always turn to the old trick of using two variables that are separated only by a logical or mathematical constant. Sounds complicated? So much the better." (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)

"Radar charts are almost always the result either of space-saving attempts or of doubtful theories about the desirability of 'symmetrical' plots, in which scores on all dimensions are similar, so giving an approximation to a circle. Their scales offer unlimited scope for manipulation in achieving this lunatic ambition." (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)

"The fact that a statement is true doesn't necessarily mean that the argument upon which is based or the chart of which it forms the action title is itself sound." (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)

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

"We tend automatically to think of all the categories represented on the horizontal axis of a column Chart as being equally important. They vary of course on the value axis. Otherwise, there would be little point in the chart, but there is somehow this feeling that they are in other respects similar members of a group. This convention can be put to good use to manipulate the message of the most boring bar or column chart." (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)