"If statistical graphics, although born just yesterday, extends its reach every day, it is because it replaces long tables of numbers and it allows one not only to embrace at glance the series of phenomena, but also to signal the correspondences or anomalies, to find the causes, to identify the laws." (Émile Cheysson, cca. 1877)
"When large numbers of curves and charts are used by a corporation, it will be found advantageous to have certain standard abbreviations and symbols on the face of the chart so that information may be given in condensed form as a signal to anyone reading the charts." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919)
"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)
"While all data contain noise, some data contain signals. Before you can detect a signal, you must filter out the noise." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)
"Noise is a signal we don't like. Noise has two parts. The first has to do with the head and the second with the heart. The first part is the scientific or objective part: Noise is a signal. [...] The second part of noise is the subjective part: It deals with values. It deals with how we draw the fuzzy line between good signals and bad signals. Noise signals are the bad signals. They are the unwanted signals that mask or corrupt our preferred signals. They not only interfere but they tend to interfere at random." (Bart Kosko, "Noise", 2006)
"One person’s signal is another person’s noise and vice versa. We call this relative role reversal the noise-signal duality." (Bart Kosko, "Noise", 2006)
"A signal is a useful message that resides in data. Data that isn’t useful is noise. […] When data is expressed visually, noise can exist not only as data that doesn’t inform but also as meaningless non-data elements of the display (e.g. irrelevant attributes, such as a third dimension of depth in bars, color variation that has no significance, and artificial light and shadow effects)." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)
"Data contain descriptions. Some are true, some are not. Some are useful, most are not. Skillful use of data requires that we learn to pick out the pieces that are true and useful. [...] To find signals in data, we must learn to reduce the noise - not just the noise that resides in the data, but also the noise that resides in us. It is nearly impossible for noisy minds to perceive anything but noise in data." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015)
"Form simplification means simplifying relationships among the components of the whole, emphasizing the whole and reducing the relevance of individual components by standardizing and generalizing relationships. This results in an increased weight of useful information (signal) against useless information (noise)." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
"Before you can even consider creating a data story, you must have a meaningful insight to share. One of the essential attributes of a data story is a central or main insight. Without a main point, your data story will lack purpose, direction, and cohesion. A central insight is the unifying theme" (telos appeal) that ties your various findings together and guides your audience to a focal point or climax for your data story. However, when you have an increasing amount of data at your disposal, insights can be elusive. The noise from irrelevant and peripheral data can interfere with your ability to pinpoint the important signals hidden within its core." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)
"In addition to managing how the data is visualized to reduce noise, you can also decrease the visual interference by minimizing the extraneous cognitive load. In these cases, the nonrelevant information and design elements surrounding the data can cause extraneous noise. Poor design or display decisions by the data storyteller can inadvertently interfere with the communication of the intended signal. This form of noise can occur at both a macro and micro level." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)
"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)
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