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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.
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.
Another kind of chartjunk skews the depiction and makes it difficult to understand the real data being displayed. Examples of this type include items depicted out of scale to one another, noisy backgrounds making comparison between elements difficult in a chart or graph, and 3-D simulations in line and bar charts.
Graphs that show a trend of data should illustrate the trend accurately in its context, rather than illustrating the trend in an exaggerated or sensationalized way. In short, don't draw misleading graphs. Choose a type of graph that is appropriate for the data you are illustrating. Cartesian coordinates
The 1 in 73 million figure was also misleading as it was reached by finding the probability of a baby from an affluent, non-smoking family dying from SIDS and squaring it: this erroneously treats each death as statistically independent, assuming that there is no factor, such as genetics, that would make it more likely for two siblings to die ...
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 ...
Not applying these principles may result in misleading graphs, distorting the message, or supporting an erroneous conclusion. According to Tufte, chartjunk refers to the extraneous interior decoration of the graphic that does not enhance the message or gratuitous three-dimensional or perspective effects.
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.