"Pie charts have weaknesses and dangers inherent in their design and application. First, it is generally inadvisable to attempt to portray more than four or five categories in a circle chart, especially if several small sectors are of approximately the same size. It may be very confusing to differentiate the relative values. Secondly, the pie chart loses effectiveness if an effort is made to compare the component values of several circles, as might occur in a temporal or geographical series. [...] Thirdly, although values are measured by distances along the arc of the circle, there is a tendency to estimate values in terms of areas by size of angle. The 100-percent bar chart is often preferable to the circle chart's angle and area comparison as it is easier to divide into parts, more convenient to use, has sections that may be shaded for contrast with grouping possible by bracketing, and has an easily readable percentage scale outside the bars." (Anna C Rogers, "Graphic Charts Handbook", 1961)
"The effective communication of information in visual form, whether it be text, tables, graphs, charts or diagrams, requires an understanding of those factors which determine the 'legibility', 'readability' and 'comprehensibility', of the information being presented. By legibility we mean: can the data be clearly seen and easily read? By readability we mean: is the information set out in a logical way so that its structure is clear and it can be easily scanned? By comprehensibility we mean: does the data make sense to the audience for whom it is intended? Is the presentation appropriate for their previous knowledge, their present information needs and their information processing capacities?" (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)
"Readability in visualization helps people interpret data and make conclusions about what the data has to say. Embed charts in reports or surround them with text, and you can explain results in detail. However, take a visualization out of a report or disconnect it from text that provides context (as is common when people share graphics online), and the data might lose its meaning; or worse, others might misinterpret what you tried to show." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)
"A space-filling layout has the property that it fills all available space in the view, as the name implies. [...] ne advantage of space-filling approaches is that they maximize the amount of room available for color coding, increasing the chance that the colored region will be large enough to be perceptually salient to the viewer. A related advantage is that the available space representing an item is often large enough to show a label embedded within it, rather than needing more room off to the side. In contrast, one disadvantage of space-filling views is that the designer cannot make use of white space in the layout; that is, empty space where there are no explicit visual elements. Many graphic design guidelines pertain to the careful use of white space for many reasons, including readability, emphasis, relative importance, and visual balance." (Tamara Munzner, "Visualization Analysis and Design", 2014)
"Occlusion can be a major readability problem with scatterplots, because many dots could be overplotted on the same location. Size coding exacerbates the problem, as does the use of text labels. Continuous scatterplots use color coding at each pixel to indicate the density of overplotting, often in conjunction with transparency. Conceptually, this approach uses a derived attribute, overplot density, which can be calculated after the layout is computed. Practically, many hardware acceleration techniques sidestep the need to do this calculation explicitly." (Tamara Munzner, "Visualization Analysis and Design", 2014)
"A picture may be worth a thousand words, but not all pictures are readable, interpretable, meaningful, or relevant." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)
"Pitfall #1: not sharing your work with others prior to your presentation [...]
Pitfall #2: lack of audience engagement [...]
Pitfall #3: little or no eye contact with the audience [...]
Pitfall #4: making your work unreadable (small font) [...]
Pitfall #5: over the time limit [...]
Pitfall #6: showing too much information on a single slide [...]
Pitfall #7: failing to use appropriate data graphics to show insights [...]
Pitfall #8: showing a chart without an explanation [...]
Pitfall #9: presenting a chart without a clear takeaway [...]
Pitfall #10: showing so many variables on a single visual display that they impair the readability of the chart or graph" (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)
"When there are few data points, place the data labels directly on the data. Data density refers to the amount of data shown in a visualization through encodings (points, bars, lines, etc.). A common mistake is presenting too much data in a single data graph. The data itself can obscure the insight. It can make the chart unreadable because the data values are not discernible. Examples include: overlapping data points, too many lines in a line chart, or too many slices in a pie chart. Selecting the appropriate amount of data requires a delicate balance. It is your job to determine how much detail is necessary." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)
