✏️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)

✏️Noah Iliinsky - Collected Quotes

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

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

"A beautiful visualization has a clear goal, a message, or a particular perspective on the information that it is designed to convey. Access to this information should be as straightforward as possible, without sacrificing any necessary, relevant complexity. [...] Most importantly, beautiful visualizations reflect the qualities of the data that they represent, explicitly revealing properties and relationships inherent and implicit in the source data. As these properties and relationships become available to the reader, they bring new knowledge, insight, and enjoyment."  (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)

"The first requirement of a beautiful visualization is that it is novel, fresh, or unique. It is difficult (though not impossible) to achieve the necessary novelty using default formats. In most situations, well-defined formats have well-defined, rational conventions of use: line graphs for continuous data, bar graphs for discrete data, pie graphs for when you are more interested in a pretty picture than conveying knowledge." (Noah Iliinsky, "On Beauty", [in "Beautiful Visualization"] 2010)

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

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

"An informative visualization primarily serves the relationship between the reader and the data. It aims for a neutral presentation of the facts in such a way that will educate the reader (though not necessarily persuade him). Informative visualizations are often associated with broad data sets, and seek to distill the content into a manageably consumable form." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Bear in mind is that the use of color doesn’t always help. Use it sparingly and with a specific purpose in mind. Remember that the reader’s brain is looking for patterns, and will expect both recurrence itself and the absence of expected recurrence to carry meaning. If you’re using color to differentiate categorical data, then you need to let the reader know what the categories are. If the dimension of data you’re encoding isn’t significant enough to your message to be labeled or explained in some way - or if there is no dimension to the data underlying your use of difference colors - then you should limit your use so as not to confuse the reader." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

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

"Exploratory data visualizations are appropriate when you have a whole bunch of data and you’re not sure what’s in it. […] By contrast, explanatory data visualization is appropriate when you already know what the data has to say, and you are trying to tell that story to somebody else." (Noah Iliinsky & Julie Steele, "Designing Data Visualizations", 2011)

"In data visualization, the number one rule of thumb to bear is mind is: Function first, suave second." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Some people use infographic to refer to representations of information perceived as casual, funny, or frivolous, and visualization to refer to designs perceived to be more serious, rigorous, or academic." (Noah Iliinsky & Julie Steele, "Designing Data Visualizations", 2011)

"Practically speaking, this pattern and pattern-violation recognition has two major implications for design. The first is that readers will notice patterns and assume they are intentional, whether you planned for the patterns to exist or not. The second is that when they perceive patterns, readers will also expect pattern violations to be meaningful." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"The advantage of redundant encoding is that using more channels to get the same information into your brain can make acquisition of that information faster, easier, and more accurate." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"The best visualizations will reveal what is interesting about the specific data set you’re working with. Different data may require different approaches, encodings, or techniques to reveal its interesting aspects. While default visualization formats are a great place to start, and may come with the correct design choices pre-selected, sometimes the data will yield new knowledge when a different visualization approach or format is used." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"[...] the term infographics is useful for referring to any visual representation of data that is: (•)  manually drawn (and therefore a custom treatment of the information); (•) specific to the data at hand (and therefore nontrivial to recreate with different data); (•) aesthetically rich (strong visual content meant to draw the eye and hold interest); and (•) relatively data-poor (because each piece of information must be manually encoded)." (Noah Iliinsky & Julie Steele, "Designing Data Visualizations", 2011)

"[...] the terms data visualization and information visualization (casually, data viz and info viz) are useful for referring to any visual representation of data that is: (•) algorithmically drawn (may have custom touches but is largely rendered with the help of computerized methods); (•) easy to regenerate with different data (the same form may be repurposed to represent different datasets with similar dimensions or characteristics); (•) often aesthetically barren (data is not decorated); and (•) relatively data-rich (large volumes of data are welcome and viable, in contrast to infographics)." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

"Ultimately, the key to a successful visualization is making good design choices." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

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

"[...] you should not rely on social or cultural conventions to convey information. However, these conventions can be very powerful, and you should be aware that your reader brings them to the table. Making use of them, when possible, to reinforce your message will help you convey information efficiently. Avoid countering conventions where possible in order to avoid creating cognitive dissonance, a clash of habitual interpretation with the underlying message you are sending." (Noah Iliinsky & Julie Steel, "Designing Data Visualizations", 2011)

✏️Alan Smith - Collected Quotes

"Bar charts are effective at displaying magnitude comparisons because they require readers to make visual interpretations in one dimension only - the length (or height) of its constituent rectangles. This is usually a good thing - it’s simple to interpret and, combined with full-length tick marks, makes comparing values quick and easy. However, condensing all differences between the data being compared into a one-dimensional axis can present chart readers with problems of interpretation when there are very big differences - of many orders of magnitude - in the data being presented." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

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

"For a chart to be truly insightful, context is crucial because it provides us with the visual answer to an important question - 'compared with what'? No number on its own is inherently big or small – we need context to make that judgement. Common contextual comparisons in charts are provided by time ('compared with last year...') and place ('compared with the north...'). With ranking, context is provided by relative performance ('compared with our rivals...')." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"Just because an interesting fact is made of numbers, it doesn’t mean we have to show it on a chart. Much time and effort spent worrying about how to dress up a dull chart could be saved by realising that some data comparisons should be explained succinctly using words alone." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"Our visual perception is context-dependent; we are not good at seeing things in isolation." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"[...] there is no such thing as a perfect chart. Every chart is a design compromise, aiming to emphasise the most important relationships in a set of numbers at the expense of the less important."(Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"Scatterplots are valuable because, without having to inspect each individual point, we can see overall aggregate patterns in potentially thousands of data points. But does this density of information come at a price - just how easy are they to read? [...] The truth is such charts can shed light on complex stories in a way words alone - or simpler charts you might be more familiar with - cannot." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"Statistics are not necessarily a good determinant of underlying causes, but they can help you spot patterns - just make sure they’re helpful ones." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

"[...] using columns for time series data is a technique to be used sparingly. Data should be relatively sparse (if the above chart showed quarterly rather than annual data over the same period, the columns would simply be too thin) and the fewer data series the better (ideally just one)."

"Whatever approach you take, it’s always a good idea to define a range of reusable colour palettes so you don’t need to face the same colour design problems every time you want to create a chart or map. There will always be exceptions that require a different treatment, but it’s good to have a solid default starting point." (Alan Smith, "How Charts Work: Understand and explain data with confidence", 2022)

✏️Kenneth W Haemer - Collected Quotes

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

"If perspective must be used - and it does have proven attraction value - it should be used with restraint. A slight rather than a sharp convergence provides definite novelty with negligible distortion. Also, perspective should be used consistently: that is, the same perspective for all charts in the presentation. Any resultant overstatement or understatement of the data will thus be uniform throughout. In any event, horizontal scale rulings should be used to enable the reader to check the visual impression, and to evaluate the plottings." (Kenneth W. Haemer, "The Perils of Perspective", The American Statistician Vol. 1 (3) 1947),

"To the question "how many rulings is the 'right' number?" there is unfortunately no easy answer. Charts designed to perform the work of a large amount of tabular data, being primarily tabular in purpose, obviously require closer rulings than charts designed primarily to present a picture. But even within these two groups the decision may be influenced by the precise purpose of the chart, its size and shape, the nature of the data, the degree of reading accuracy needed, and to some extent, by the style of the medium in which the chart appears." (Kenneth W Haemer, "Hold That Line. A Plea for the Preservation of Chart Scale Ruling", The American Statistician Vol. 1 (1) 1947)

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

"[…] many readers are confused by the presence of two scales, and either use the wrong one or simply disregard both. Also, the general reader has the disconcerting habit of believing that because one curve is higher than another, it is also larger in magnitude. This leads to all sorts of misconceptions." (Kenneth W Haemer, "Double Scales Are Dangerous", The American Statistician Vol. 2 (3) , 1948)

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

"Seeing color isn't always as simple as it may seem. Some colors are not easy to see unless the conditions are just right; some are so easy to see that they overpower everything else; some are easy to see but difficult to distinguish. […] Large masses of color become too visible and easily overwhelm the entire chart. The more visible the color the easier it is to use too much of it." (Kenneth W Haemer, "Color in Chart Presentation", The American Statistician Vol. 4 (2) , 1950)

✏️Jennifer George-Palilonis - Collected Quotes

"[…] a graphic with loose, incomplete information that is too verbose, vague or passive can actually impede your audience’s ability to make sense of the information at hand. If the graphic confuses or frustrates the audience, you’re likely to do more harm than good, leave them with more questions than answers and essentially turn them away from your publication." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Actually composing an information graphic - putting all of the pieces together in a rhythmic, orderly, interesting design - is equal in importance to writing the text and creating the main illustrations. In fact, the design of the graphic can have a direct impact on an audience’s ability to follow the information that is presented in an efficient and logical manner. Design can also affect the level of meaning and understanding an audience will take away from the graphic. Thus, understanding how to compose/design an information graphic is paramount to a graphics reporter’s ability to succeed." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"An infographic’s headline should summarize the main point of the presentation. Any introductory text or 'chatter' should explain the most newsworthy information within the context of the visual story being told; i.e., is the what of the story most important? Is the how of the story most important?, etc." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Believe it or not, it’s easy to make statistics lie. It’s called massaging the facts, and people do it all the time. […] To avoid this, graphics reporters should develop a keen eye for spotting problems with statistics in order to avoid the embarrassment and possible liability of reporting incorrect information." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Graphics should be planned, written and developed to stand alone. Even when a graphic is accompanied by a story, we can’t always count on the reader to get that far. Scanning readers often don’t engage with stories at all. Rather, they browse the page, often reading only display type and visual elements. And, even those who intend to read the story often engage with the graphics first because they tend to be more eye-catching. In both cases, you simply can’t create a graphic that isn’t complete without the story. Readers should finish an information graphic feeling confident that they understand the information it presents. This isn’t to say that you must tell the entire story with the graphic. However, the portions of the story that are represented in the graphic must be complete and clear." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Just as rhythm in music can move you to dance, sway or tap your foot, visual rhythm is the combination and arrangement of elements that moves your eyes through a graphic presentation. Visual rhythm can be achieved by repeating patterns that are similar in size, shape or color, by alternating elements that contrast one another in some way or by placing elements in a manner that creates progression, such as small to large or light to dark." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Look for comparisons, dates or other organizational facts outlined in the story. Who are the key players, and why? What are the key dates? How did we get here? Where do we go from here? What’s at issue, and what does it mean for the reader? These types of questions often lead to discovering graphics potential for a story, and by presenting the answers in a graphic manner, you provide readers with a quickly accessible and easily understood context for the rest of the story." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Make use of a simple data metaphor. Regardless of the concept you are trying to convey with an information graphic, you must make sure that the visual metaphor (i.e., a circle to represent a whole, as with a pie chart) be clear and logical. Don’t get so caught up in being clever that you make illogical comparisons or use unclear metaphors. In other words, don’t make your readers have to think too hard to get the point. They’ll appreciate you for it!" (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"Proportion is important to information graphics because it helps create a sense of hierarchy and order among the elements. […] Proportion is also achieved by incorporating elements of varying sizes or shapes in a layout. This practice allows us to compare them to one another and make visual judgments about their relative sizes and shapes or proportion. Adhering to proportional size and shape relationships will result in a more interesting overall visual effect than if all elements are more or less the same size. Proportion is also useful in contributing to a sense of depth." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

"[…] rhythm can be achieved in a variety of different ways. Asymmetrical balance is most commonly used in the design of graphics because it is the most effective way to move the eye around a graphic. Repetition in the placement of like elements or even the same element can also establish rhythm in a graphic. The similarity of the elements makes a visual connection for the eye and moves it from one to the next. Chronological, numerical or alphabetic placement of elements is also a simple way to create rhythm. This placement creates an obvious order for the eye to follow. Finally, integrating visual elements that are directional in nature often helps lead the eye in a specific direction. This could be something as simple as the use of an arrow in a design." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

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

"Text should provide the information and context that visuals cannot. By their nature, visuals can be ambiguous; well-written sentences are not. Infographics - whether statistical, cartographic or diagrammatic - are meant to demonstrate data visually and holistically. So the visuals in an infographic should do as much explanatory 'lifting' as possible, allowing words only to qualify, specify, summarize and organize." (Jennifer George-Palilonis," A Practical Guide to Graphics Reporting: Information Graphics for Print, Web & Broadcast", 2006)

✏️Dennis K Lieu - Collected Quotes

"Being a good team member takes work. Most people are used to working on their own - making decisions, prioritizing tasks, and being accountable for their own work. Working with others requires a different approach than working alone. To be a successful part of a team, you need to consider several issues. You should be prepared not to be in charge of everything. For some people, this requires a great deal of effort; for other people, it is less taxing. At times, you will be the supervisor; other times you will be supervised. You need to be flexible and understand that a team consisting only of leaders (or only of followers) is not likely to perform well." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

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

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

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

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

"Most importantly, prepare to learn how to be a team member. Share your strengths with the team and be willing to contribute. Remember, the combined efforts of all team members should yield a better outcome than the efforts of one individual. Learn new team skills and be adaptable." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Reverse engineering is a systematic methodology for analyzing the design of an existing device or system, either as an approach to study the design or as a prerequisite for redesign. Reverse engineering essentially is a process used to gain information about the functionality and sizes of existing design components. [...] Reverse engineering is a technique within the practice of engineering design that can be useful in several ways. Reverse engineering can save time because there is no need to 'reinvent the wheel' when you can start from existing geometric data. The reverse engineering technique also can help an engineer develop a systematic approach to thinking about and improving the design of devices and systems." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Tables work in a variety of situations because they convey large amounts of data in a condensed fashion. Use tables in the following situations: (1) to structure data so the reader can easily pick out the information desired, (2) to display in a chart when the data contains too many variables or values, and (3) to display exact values that are more important than a visual moment in time." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"The data [in tables] should not be so spaced out that it is difficult to follow or so cramped that it looks trapped. Keep columns close together; do not spread them out more than is necessary. If the columns must be spread out to fit a particular area, such as the width of a page, use a graphic device such as a line or screen to guide the reader’s eye across the row." (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

"Whereas charts generally focus on a trend or comparison, tables organize data for the reader to scan. Tables present data in an easy-read-format, or matrix. Tables arrange data in columns or rows so readers can make side-by-side comparisons. Tables work for many situations because they convey large amounts of data and have several variables for each item. Tables allow the reader to focus quickly on a specific item by scanning the matrix or to compare multiple items by scanning the rows or columns."  (Dennis K Lieu & Sheryl Sorby, "Visualization, Modeling, and Graphics for Engineering Design", 2009)

✏️Carl T Bergstrom - Collected Quotes

"[...] although numbers may seem to be pure facts that exist independently from any human judgment, they are heavily laden with context and shaped by decisions - from how they are calculated to the units in which they are expressed." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

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

"Confirmation bias is the tendency to notice, believe, and share information that is consistent with our preexisting beliefs. When a claim confirms our beliefs about the world, we are more prone to accept it as true and less inclined to challenge it as possibly false." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Correlation doesn't imply causation - but apparently it doesn't sell newspapers either."(Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

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

"If the data that go into the analysis are flawed, the specific technical details of the analysis don’t matter. One can obtain stupid results from bad data without any statistical trickery. And this is often how bullshit arguments are created, deliberately or otherwise. To catch this sort of bullshit, you don’t have to unpack the black box. All you have to do is think carefully about the data that went into the black box and the results that came out. Are the data unbiased, reasonable, and relevant to the problem at hand? Do the results pass basic plausibility checks? Do they support whatever conclusions are drawn?" (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"If you study one group and assume that your results apply to other groups, this is extrapolation. If you think you are studying one group, but do not manage to obtain a representative sample of that group, this is a different problem. It is a problem so important in statistics that it has a special name: selection bias. Selection bias arises when the individuals that you sample for your study differ systematically from the population of individuals eligible for your study." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Jargon may facilitate technical communication within a field, but it also serves to exclude those who have not been initiated into the inner circle of a discipline." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Machines are not free of human biases; they perpetuate them, depending on the data they’re fed. [...] When we train machines to make decisions based on data that arise in a biased society, the machines learn and perpetuate those same biases." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Mathiness refers to formulas and expressions that may look and feel like math-even as they disregard the logical coherence and formal rigor of actual mathematics. […] These equations make mathematical claims that cannot be supported by positing formal relationships - variables interacting multiplicatively or additively, for example - between ill-defined and impossible-to-measure quantities. In other words, mathiness, like truthiness and like bullshit, involves a disregard for logic or factual accuracy." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

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

"People do care about how they are measured. What can we do about this? If you are in the position to measure something, think about whether measuring it will change people’s behaviors in ways that undermine the value of your results. If you are looking at quantitative indicators that others have compiled, ask yourself: Are these numbers measuring what they are intended to measure? Or are people gaming the system and rendering this measure useless?" (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"Reporting numbers as percentages can obscure important changes in net values. […] Percentage calculations can give strange answers when any of the numbers involved are negative." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"So what does it mean to tell an honest story? Numbers should be presented in ways that allow meaningful comparisons." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"The problem is the hype, the notion that something magical will emerge if only we can accumulate data on a large enough scale. We just need to be reminded: Big data is not better; it’s just bigger. And it certainly doesn’t speak for itself." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"There are many ways for error to creep into facts and figures that seem entirely straightforward. Quantities can be miscounted. Small samples can fail to accurately reflect the properties of the whole population. Procedures used to infer quantities from other information can be faulty. And then, of course, numbers can be total bullshit, fabricated out of whole cloth in an effort to confer credibility on an otherwise flimsy argument. We need to keep all of these things in mind when we look at quantitative claims. They say the data never lie - but we need to remember that the data often mislead." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"This problem with adding additional variables is referred to as the curse of dimensionality. If you add enough variables into your black box, you will eventually find a combination of variables that performs well - but it may do so by chance. As you increase the number of variables you use to make your predictions, you need exponentially more data to distinguish true predictive capacity from luck." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

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

"We all know that the numerical values on each side of an equation have to be the same. The key to dimensional analysis is that the units have to be the same as well. This provides a convenient way to keep careful track of units when making calculations in engineering and other quantitative disciplines, to make sure one is computing what one thinks one is computing. When an equation exists only for the sake of mathiness, dimensional analysis often makes no sense." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

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

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

✏️John Hoffmann - Collected Quotes

"A useful way to think about tables and graphics is to visualize layers. Just as photographic files may be manipulated in photo editing software using layers, data presentations are constructed by imagining that layers of an image are placed one on top of another. There are three general layers that apply to visual data presentations: (a) a frame that is typically a rectangle or matrix, (b) axes and coordinate systems (for graphics), and (c) data presented as numbers or geometric objects." (John Hoffmann, "Principles of Data Management and Presentation", 2017)

"Also known as line charts or line plots, this type of graphic displays a series of data points using line segments. […] Do not include too many lines, especially if they are difficult to distinguish. […] it is best to label the lines directly rather than use a legend. […] It is not a good idea to use line graphs with unordered categorical (nominal) data These graphs are simpler to understand when the data are ordered in some way. […] Visual acuity is enhanced when the lines do not touch the x- or y-axis […] There is no need, except under exceptional circumstances, to include a marker to show at what point the line matches a specific value of the x- and y-axes. Line graphs are designed to display patterns and trends rather than data points." (John Hoffmann, "Principles of Data Management and Presentation", 2017)

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

"Contrasts can be a help or a hindrance. Our eyes are drawn to bright colors on muted backgrounds. In addition, warm colors, such as red, are more likely to get attention than cool colors (although the relative brightness affects this phenomenon). Objects in color that are included in black and white or grayscale visuals are quite effective at drawing the eye. Thus, using color to highlight certain parts of a graphic or table can be valuable. However, avoid using these strategies if they will draw attention to extraneous or trivial parts of the data presentation." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

"If colors are used for different bars in a graphic, use distinguishable shades of the same color rather than distinct colors. If lines are in color in a graph, use those that are easy to discriminate, such as red and blue. But be careful of lines that cross since a red line is perceived as in front of a blue line. If colors are employed in a table, used them to highlight the relevant comparisons you wish to make. […] Use colors to highlight important parts of the graphic. […] But be careful because this practice is easily abused." (John Hoffmann, "Principles of Data Management and Presentation", 2017)

"It is generally a good idea to avoid gridlines, vertical lines, and double lines. Use single horizontal lines to separate the title, headers, and content. Lines are also employed to identify column spanners, which are used to group particular columns of data." (John Hoffmann, "Principles of Data Management and Presentation", 2017)

"Many data presentations spice up the image with background images, embedded visuals, ornate typeface, and bright colors. Our eyes may be drawn to these aspects, rather than to the patterns in the data, thus breaking the principles of clarity and efficiency. It is usually best to take out the clutter: remove the chartjunk." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

"People tend to comprehend visual images quicker and with fewer errors than words on a page. Visual images also activate memories better than words." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

"Reference tables show a lot of data with a high degree of precision. They are designed generally to provide users with a way to fi nd particular pieces of data. […] Summary tables provide some type of extraction of data from a reference table or a spreadsheet. The data are usually manipulated, analyzed, or summarized in some way, such as by sorting or providing summary statistics (means, percentages, ranges). The results of statistical models are usually presented in research reports using this type of table." (John Hoffmann, "Principles of Data Management and Presentation", 2017)

"Some experts argue that axes - in particular, the y-axis - should always begin at zero. However, when differences are small, yet the size of the numbers is relatively large, this can make detection difficult. On the other hand, viewers can be misled by manipulating the axes to magnify differences. One guideline is to always use a zero bottom point when judging absolute magnitudes. This is often the case in bar charts." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

"Titles should clearly specify the content of the table or the graphic. What is being presented? Means and standard deviations? Confidence intervals? Percentages? Trends over time? Furthermore, consider the context, such as when and where the data were gathered, as well as the name of the dataset if using secondary data (although the dataset may also be identified in a source note)." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

"Whichever scale is used to represent the data, it is important to keep it consistent in data presentations. The principles of clarity, precision, and efficiency are rarely met if the measurement scales change within tables." (John Hoffmann, "Principles of Data Management and Presentation", 2017) 

✏️Tamara Munzner- Collected Quotes

"A fundamental principle of design is to consider multiple alternatives and then choose the best, rather than to immediately fixate on one solution without considering any alternatives. One way to ensure that more than one possibility is considered is to explicitly generate multiple ideas in parallel. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"As with all design problems, vis design cannot be easily handled as a simple process of optimization because trade-offs abound. A design that does well by one measure will rate poorly on another. The characterization of trade-offs in the vis design space is a very open problem at the frontier of vis research." (Tamara Munzner, "Visualization Analysis and Design", 2014)

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

"Interactivity is crucial for building vis tools that handle complexity. When datasets are large enough, the limitations of both people and displays preclude just showing everything at once; interaction where user actions cause the view to change is the way forward. Moreover, a single static view can show only one aspect of a dataset. For some combinations of simple datasets and tasks, the user may only need to see a single visual encoding. In contrast, an interactively changing display supports many possible queries. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"Statistical characterization of datasets is a very powerful approach, but it has the intrinsic limitation of losing information through summarization. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"The effectiveness principle dictates that the importance of the attribute should match the salience of the channel; that is, its noticeability. In other words, the most important attributes should be encoded with the most effective channels in order to be most noticeable, and then decreasingly important attributes can be matched with less effective channels. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"The expressiveness principle dictates that the visual encoding should express all of, and only, the information in the dataset attributes. The most fundamental expression of this principle is that ordered data should be shown in a way that our perceptual system intrinsically senses as ordered. Conversely, unordered data should not be shown in a way that perceptually implies an ordering that does not exist. Violating this principle is a common beginner’s mistake in vis. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"The idiom of heatmaps is one of the simplest uses of the matrix alignment: each cell is fully occupied by an area mark encoding a single quantitative value attribute with color. […] The benefit of heatmaps is that visually encoding quantitative data with color using small area marks is very compact, so they are good for providing overviews with high information density. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

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

"The idiom of scatterplots encodes two quantitative value variables using both the vertical and horizontal spatial position channels, and the mark type is necessarily a point. Scatterplots are effective for the abstract tasks of providing overviews and characterizing distributions, and specifically for finding outliers and extreme values. Scatterplots are also highly effective for the abstract task of judging the correlation between two attributes. With this visual encoding, that task corresponds the easy perceptual judgement of noticing whether the points form a line along the diagonal. The stronger the correlation, the closer the points fall along a perfect diagonal line; positive correlation is an upward slope, and negative is downward." (Tamara Munzner, "Visualization Analysis and Design", 2014)

"The most powerful depth cue is occlusion, where some objects can not be seen because they are hidden behind others. The visible objects are interpreted as being closer than the occluded ones. The occlusion relationships between objects change as we move around; this motion parallax allows us to build up an understanding of the relative distances between objects in the world. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"The phenomenon of change blindness is that we fail to notice even quite drastic changes if our attention is directed elsewhere. […] Although we are very sensitive to changes at the focus of our attention, we are surprisingly blind to changes when our attention is not engaged. The difficulty of tracking complex and widespread changes across multiframe animations is one of the implications of change blindness for vis. " (Tamara Munzner, "Visualization Analysis and Design", 2014)

"Three high-level targets are very broadly relevant, for all kinds of data: trends, outliers, and features. A trend is a high-level characterization of a pattern in the data. Simple examples of trends include increases, decreases, peaks, troughs, and plateaus. Almost inevitably, some data doesn’t fit well with that backdrop; those elements are the outliers. The exact definition of features is task dependent, meaning any particular structures of interest." (Tamara Munzner, "Visualization Analysis and Design", 2014)

✏️John M Chambers - Collected Quotes

"At the heart of probabilistic statistical analysis is the assumption that a set of data arises as a sample from a distribution in some class of probability distributions. The reasons for making distributional assumptions about data are several. First, if we can describe a set of data as a sample from a certain theoretical distribution, say a normal distribution (also called a Gaussian distribution), then we can achieve a valuable compactness of description for the data. For example, in the normal case, the data can be succinctly described by giving the mean and standard deviation and stating that the empirical (sample) distribution of the data is well approximated by the normal distribution. A second reason for distributional assumptions is that they can lead to useful statistical procedures. For example, the assumption that data are generated by normal probability distributions leads to the analysis of variance and least squares. Similarly, much of the theory and technology of reliability assumes samples from the exponential, Weibull, or gamma distribution. A third reason is that the assumptions allow us to characterize the sampling distribution of statistics computed during the analysis and thereby make inferences and probabilistic statements about unknown aspects of the underlying distribution. For example, assuming the data are a sample from a normal distribution allows us to use the t-distribution to form confidence intervals for the mean of the theoretical distribution. A fourth reason for distributional assumptions is that understanding the distribution of a set of data can sometimes shed light on the physical mechanisms involved in generating the data." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

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

"Frequently we can increase the informativeness of a graph by removing structure from the data once we have identified it, so that subsequent plots are free of its dominating influence and can help us see finer structure or subtler effects. This usually means (l) partitioning the data, or (2) plotting differences or ratios, or (3) fitting a model and taking the residuals as a new set of data for further study." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Generally speaking, a good display is one in which the visual impact of its components is matched to their importance in the context of the analysis. Consider the issue of overplotting." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

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

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

"Part of the strategy of regression modelling is to improve the model until the residuals look 'structureless', or like a simple random sample. They should only contain structure that is already taken into account (such as nonconstant variance) or imposed by the fitting process itself. By plotting them against a variety of original and derived variables, we can look for systematic patterns that relate to the model's adequacy. Although we talk about graphics for use after the model is fit, if problems with the fit are discovered at this stage of the analysis, We should take corrective action and refit the equation or a modified form of it." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

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

"Symmetry is also important because it can simplify our thinking about the distribution of a set of data. If we can establish that the data are (approximately) symmetric, then we no longer need to describe the  shapes of both the right and left halves. (We might even combine the information from the two sides and have effectively twice as much data for viewing the distributional shape.) Finally, symmetry is important because many statistical procedures are designed for, and work best on, symmetric data." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"The information on a plot should be relevant to the goals of the analysis. This means that in choosing graphical methods we should match the capabilities of the methods to our needs in the context of each application. [...] Scatter plots, with the views carefully selected as in draftsman's displays, casement displays, and multiwindow plots, are likely to be more informative. We must be careful, however, not to confuse what is relevant with what we expect or want to find. Often wholly unexpected phenomena constitute our most important findings." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"The most important reason for portraying standard deviations is that they give us a sense of the relative variability of the points in different regions of the plot." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

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

"The truth is that one display is better than another if it leads to more understanding. Often a simpler display, one that tries to accomplish less at one time, succeeds in conveying more insight. In order to understand complicated or subtle structure in the data we should be prepared to look at complicated displays when necessary, but to see any particular type of structure we should use the simplest display that shows it."(John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

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

"We can gain further insight into what makes good p!ots by thinking about the process of visual perception. The eye can assimilate large amounts of visual information, perceive unanticipated structure, and recognize complex patterns; however, certain kinds of patterns are more readily perceived than others. If we thoroughly understood the interaction between the brain, eye, and picture, we could organize displays to take advantage of the things that the eye and brain do best, so that the potentially most important patterns are associated with the most easily perceived visual aspects in the display." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"When some interesting structure is seen in a plot, it is an advantage to be able to relate that structure back to the original data in a clear, direct, and meaningful way. Although this seems obvious, interpretability is at once one of the most important, difficult, and controversial issues." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)