How can scatter plots be misleading and what can you do to clarify their meaning?

1 Scale Skewing

The choice of scale can dramatically affect the interpretation of a scatter plot. If you use scales that are too wide, important variations in the data might become invisible, making it seem like there's little to no correlation when there might be a significant one. Conversely, very narrow scales can exaggerate correlations, misleading viewers into seeing a strong relationship where there is only a weak one. To avoid these issues, choose scales that represent your data accurately. Ensure that they're neither too compressed nor overly expanded. If necessary, consider using logarithmic or other nonlinear scales to better represent the data.

2 Outlier Influence

Outliers are data points that fall far outside the general spread of data. They can have a disproportionate effect on a scatter plot, either by creating the illusion of a trend or by distorting an existing one. To clarify the meaning of your scatter plot, it's essential to investigate outliers and decide how to handle them. You might choose to include them with annotations explaining their context, or you might exclude them from the plot if they're due to errors or are not representative of the dataset.

3 Aspect Ratio

The aspect ratio of a scatter plot, which is the ratio of its width to its height, can mislead viewers about the strength of a relationship between variables. A plot that's too tall or too wide can distort the viewer's perception. To present your data accurately, maintain an aspect ratio that reflects the true nature of the relationship. A 1:1 aspect ratio, where one unit on the x-axis is equal to one unit on the y-axis, is often a good starting point, especially for comparing variables that have the same units.

4 Data Density

In scatter plots with many data points, areas of high data density can become obscured, leading to misinterpretation of the data's distribution. Overlapping points can give the false impression that there is less variance than there actually is. To mitigate this, you could use transparency to allow for overlapping data points to be visible or apply data density techniques like hexbin plots or 2D histograms that group points into hexagonal or rectangular bins, revealing the underlying distribution patterns.

5 Trend Lines

Adding a trend line to a scatter plot can help illustrate the relationship between variables, but it can also be misleading if not used carefully. A trend line that doesn't match the nature of the data—for instance, using a linear trend line for nonlinear data—can give a false impression of the relationship. When adding a trend line, ensure it is appropriate for the data distribution. Consider using polynomial or spline fits for more complex relationships and always provide context for what the trend line represents.

6 Interpretation Aids

To enhance clarity in scatter plots, incorporate interpretation aids such as labels, legends, and annotations. Labels for axes should be clear and concise, while legends are necessary for plots with multiple groups of data. Annotations can help explain specific data points or trends that aren't immediately obvious. Interactive elements like tooltips that appear when you hover over data points can provide additional details without cluttering the visual space. By carefully designing these aids, you can make your scatter plots not only visually appealing but also informative and easy to understand.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?