Question

A) Give one example of distortion or unnecessary detail in a visualization , explain the problem and suggest a solution

B) Find one example of misleading interpretation , explain the problem and suggest a solution

Answer 1

Distortion or unnecessary detail in visualization

The research team conducted a series of user studies to test the effect of common visual distortions on audience interpretation of the visualization message — and to determine how severe the different distortion techniques are in terms of deceiving the user.

The study results suggest that when presented with a deceptive visualization which intentionally exaggerated the message participants did indeed perceive the underlying message in its exaggerated form. Similarly, participants presented with a visualization that suggested a reversal of the message to be drawn from the underlying data were also deceived at a very high rate.

Omitting Data
Examples of misleading visualisation for omitting data and their interpretations

trends that don’t actually exist can easily be created whereas some existing highlights can go unnoticed. That’s because by omitting some data we are missing the context. Leaving out variables can affect how you interpret the data and what conclusions you draw from it. So whenever you’re examining a variable and its relationships, carefully consider the context in which that variable exists and deliberately seek out other variables that could affect the one you’re studying.

As an example of what happens when you omit some data, be that because you purposefully want to create a misleading data visualization or you simply want to make your work easier, take a look at the scatter plot below. By leaving out some data points, the chart that normally would be filled with dips and spikes, looks much smoother and more stable. See these graphs

Interpretation

only plotting every second year instead of every year, the graph appears to have a steady increase, while the real data is more volatile. Companies can take advantage of this by omitting years with significant changes in sales to make their earnings look constant and predictable, masking the true volatility of the market. When evaluating data visualizations make sure to have all the data accessible.

Misleading Data

If you want your data to tell the whole truth and nothing but the truth, implement these practices to make sure you avoid misleading data visualization.