"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)
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