Regarding badly designed graphs, especially the difficulties with 3D graphs, I agree with your observations. Accurate reading and interpretation of a 3D graph can often be challenging due to its complexity and abundance of data. Understanding the data requires the ability to perceive graphics, which is further complicated by tilting one's head to read the information. Inaccurate interpretations of poorly designed graphs may mislead readers and others with whom they share the information, as in the case of training sessions or classrooms. It's true that a more straightforward option, such as a 2D bar graph, would work better. Less numbering and a sideways-oriented bar graph enhance readability and comprehension. To prevent confusion brought on by spread values and three-dimensional cylinder bars, each percentage in a hematocrit group should have a distinct number (Atlassian, 2024). Bar graphs are useful because they can be easily updated and effectively show trends. Although they are usually applied to categorical data, they can also effectively display means and standard errors or deviations for continuous data in small-sample studies. What other ways do you think we can enhance data comprehension through graph design? -Meredyth Reference Atlassian. (2024). Stacked Bar Charts: A Detailed Breakdown. Atlassian. https://www.atlassian.com/data/charts/stacked-bar-chart-complete-guide