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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.
Examples of unnecessary elements that might be called chartjunk include heavy or dark grid lines, unnecessary text, inappropriately complex or gimmicky font faces, ornamented chart axes, and display frames, pictures, backgrounds or icons within data graphs, ornamental shading and unnecessary dimensions.
Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.
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.
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 ...
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
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) :
A bar graph shows comparisons among discrete categories. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value. Some bar graphs present bars clustered in groups of more than one, showing the values of more than one measured variable. These clustered groups can be differentiated using ...