Data visualization relies substantively on authors' choices of axes arrangement, such as truncation and scaling, as well as the shapes, including figure types and relative sizes.
Apparently, if the obtained data has significant biases, fake information, or simply a large number of missing data, then the visualization would not be as clear and perceptive as those developed from comprehensive datasets. Leaving aside the influence the data quality and looking at the visualization methods only, how to choose the appropriate axis scaling and truncation, as well as the displaying shape is critical to the quality of any data visualization.
First, the importance of not truncating bars and scaling in line charts is emphasized in the articles. Changes in these aspects can lead to misunderstanding of the original information intended to show, and of course, sometimes are used deliberately to "deceive" the readers for promotional or other purposes. Second, the concept of "proportional ink" is another substantive guiding rule that data visualizers should always keep in mind. The perception of data visualization requires integration with the psychological interpretability of humans on visual figures. Accuracy should not give way to aesthetics. It is essential to find out the most interpretable form of presenting the data, not just simply altering the inept choice and give away accuracy to gain more awareness from the audience.