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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.
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.
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 ...
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.
An example of a chart containing gratuitous chartjunk. This chart uses a large area and much "ink" (many symbols and lines) to show only five hard-to-read numbers, 1, 2, 4, 8, and 16. Chartjunk consists of all visual elements in charts and graphs that are not necessary to comprehend the information represented on the graph, or that distract the ...
California Gov. Gavin Newsom ordered state agencies to recommend ways to reduce health harms and limit the purchase of candy, soda and other ultra-processed foods.
Some scholars classify cherry-picking as a fallacy of selective attention, the most common example of which is the confirmation bias. [3] Cherry picking can refer to the selection of data or data sets so a study or survey will give desired, predictable results which may be misleading or even completely contrary to reality. [4]