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In statistics, a misleading graph, also known as a distorted graph, is a graph that misrepresents data, constituting a misuse of statistics and with the result that an incorrect conclusion may be derived from it. Graphs may be misleading by being excessively complex or poorly constructed.
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Manipulation of the graph's X-axis can also mislead; see the graph to the right. Both graphs are technically accurate depictions of the data they depict, and do use 0 as the base value of the Y-axis; but the rightmost graph only shows the "trough"; so it would be misleading to claim it depicts typical data over that time period.
A graph or chart or diagram is a diagrammatical illustration of a set of data. If the graph is uploaded as an image file, it can be placed within articles just like any other image. Graphs must be accurate and convey information efficiently. They should be viewable at different computer screen resolutions.
Apparel provides an example. People have a wide range of sizes and body shapes. It is obvious that apparel sizing must be multidimensional. Instead it is complex in unexpected ways. Some apparel is sold by size only (with no explicit consideration of body shape), sizes vary by country and manufacturer and some sizes are deliberately misleading ...
It also shows how statistical graphs can be used to distort reality. For example, by truncating the bottom of a line or bar chart so that differences seem larger than they are. Or, by representing one-dimensional quantities on a pictogram by two- or three-dimensional objects to compare their sizes so that the reader forgets that the images do ...
Simpson's paradox has been used to illustrate the kind of misleading results that the misuse of statistics can generate. [7] [8] Edward H. Simpson first described this phenomenon in a technical paper in 1951, [9] but the statisticians Karl Pearson (in 1899 [10]) and Udny Yule (in 1903 [11]) had mentioned similar effects earlier.
Some graphs include several orders of magnitude, showing both very small and very large numbers. Small numbers disappear in the shadow of the large numbers. Examples: Solar_power#Development_and_deployment and Offshore_wind_power#History. Same graph in logarithmic (hand calculated, and showing logarithmic numbers, not original data) :