"Except in some of the simplest cases where the line connecting the plotted data is straight, it will generally be possible to fit a number of very different forms of equation to the same curve, none of them exactly, but all agreeing with the original about equally well. Interpolation on any of these curves will usually give results within the desired degree of accuracy. The greatest caution, however, should be observed in exterpolation, or the use of the equation outside of the limits of the observations." (John B Peddle, "The Construction of Graphical Charts", 1910)
"In fitting an equation to a given set of observations the first step is to draw through the plotted points a smooth curve. If the experimental work has been carefully and accurately done the curve may be made to pass through, or close to, almost all the points. If not, the curve must be drawn in such a way as to represent a good probable average; that is, so as to lea:ve about an equal number of points at about equal distances on either side of it, these distances, of course, being kept as small as possible. Such a curve is assumed to represent the most probable values of the observations, and we then attempt to get its equation." (John B Peddle, "The Construction of Graphical Charts", 1910)
"In getting an algebraic expression to show the relations between the components of a given set of data there may be two entirely distinct objects in view, one being to determine the physical law controlling the results and the other to get a mathematical expression, which may or may not have a physical basis, but which will enable us to calculate in a more or less accurate manner other results of a nature similar to those of the observations." (John B Peddle, "The Construction of Graphical Charts", 1910)
"In all chart-making, the material to be shown must be accurately compiled before it can be charted. For an understanding of the classification chart, we must delve somewhat into the mysteries of the various methods of classification and indexing. The art of classifying calls into play the power of visualizing a 'whole' together with all its 'parts'. Even in the most exact science, it is not always easy to break up a whole into a complete set of the distinct, mutually exclusive parts which together exactly compose it." (Karl G Karsten, "Charts and Graphs", 1925)
"In short, the rule that no more dimensions or axes should be used in the chart than the data calls for, is fundamental. Violate this rule and you bring down upon your head a host of penalties. In the first place, you complicate your computing processes, or else achieve a grossly deceptive chart. If your chart becomes deceptive, it has defeated its purpose, which was to represent accurately. Unless, of course, you intended to deceive, in which case we are through with you and leave you to Mark Twain’s mercies. If you make your chart accurate, at the cost of considerable square or cube root calculating, you still have no hope, for the chart is not clear; your reader is more than likely to misunderstand it. Confusion, inaccuracy and deception always lie in wait for you down the path departing from the principle we have discussed - and one of them is sure to catch you." (Karl G Karsten, "Charts and Graphs", 1925)
"Although arguments can be made that high data density does not imply that a graphic will be good, nor one with low density bad, it does reflect on the efficiency of the transmission of information. Obviously, if we hold clarity and accuracy constant, more information is better than less. One of the great assets of graphical techniques is that they can convey large amounts of information in a small space." (Howard Wainer, "How to Display Data Badly", The American Statistician Vol. 38(2), 1984)
"Of course increased bias does not necessarily imply less overall accuracy. The reasoning, however, is that the mechanism leading to bias might well lead to other types of inaccuracy as well." (William S Cleveland & Robert McGill, "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods", Journal of the American Statistical Association Vol. 79(387), 1984)
"The aim of good data graphics is to display data accurately and clearly. Let us use this definition as a starting point for categorizing methods of bad data display. The definition has three parts. These are (a) showing data, (b) showing data accurately, and (c) showing data clearly." (Howard Wainer, "How to Display Data Badly", The American Statistician Vol. 38(2), 1984)
"One must be careful not to fall into a conceptual trap by adopting accuracy as a criterion. We are not saying that the primary purpose of a graph is to convey numbers with as many decimal places as possible. […] The power of a graph is its ability to enable one to take in the quantitative information, organize it, and see patterns and structure not readily revealed by other means of studying the data." (William S Cleveland & Robert McGill, "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods", Journal of the American Statistical Association Vol. 79(387), 1984)
"For every rule in data visualization, there is a scenario where that rule should be broken. This means that choosing the best chart or the best design is always a trade-off between several conflicting goals. Our imperfect perception means that data visualization has a larger subjective dimension than a data table. Sometimes we only need this subjective, impressionist dimension and other times we need to translate it into hard figures. Striving for accuracy is important, but it’s more important to provide those insights that only a visual display can reveal." (Jorge Camões, "Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel", 2016)
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