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[MUSIC]. 
In this lecture we're going to go through 

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the step by step process of how to 
evaluate a visualzation. 

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The first step is to consider the purpose 
of the visualization and who the intended 

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audience is. 
In other words, is it to educate, to 

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inform, or is it for exploration? 
Is the audience the general public, or a 

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specialized body of scientists, for 
example? 

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That's very important in deciding what 
the overall value is of this particular 

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type of visualization. 
Don't make the mistake of judging a 

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visualization without understanding the 
intended audience. 

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Next determine your initial reaction. 
Is it generally positive or negative? 

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It's all right to have an emotional 
reaction to a visualization, but also try 

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to step back and analyze it somewhat 
dispassionately. 

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Examine the visualization in detail. 
What works well, what doesn't. 

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And finally answer questions such as the 
following. 

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Is the design aesthetically pleasing? 
Is it immediately understandable? 

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If not, is it understandable after a 
short period of the study. 

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Does it provide insight or understanding 
that was not obtainable with the original 

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representation text or table, etcetera. 
This is very important. 

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If the only difference between the 
visualization is that somebody added 

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color to the design and made a pie chart 
out of it, that does not necessarily make 

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it valueable. 
A visualization should add value over a 

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textual representation or it is not a 
good visualization. 

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Fourth, does it provide insight or 
understanding better than some 

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alternative visualization would, or does 
it require excessive cognitive effort? 

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What kind of visualization might have 
been better? 

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For example, if somebody has given you a 
set of multiple pie charts, and you have 

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to compare pie wedges from one to the 
other. 

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Is that really the best way to make those 
comparisons? 

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Or could a bar chart have done the job 
just as well? 

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Maybe better. 
Does the visualization reveal trends, 

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patterns, gaps, or outliers? 
Can the viewer make effective 

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comparisons? 
Does the visualization successfully 

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highlight important information while 
providing context for that information? 

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Is new information gleaned from the 
visualization that wouldn't have come 

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from an alternative method? 
And is the important information 

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highlighted? 
And is the context not hidden? 

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Number seven, does it distort the 
information? 

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If it transforms it in some ways, this 
misleading, or hopefully simplifying. 

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Does it omit important information? 
Is it memorable? 

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Does it use visual components properly? 
That is, does it represent the data using 

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lines, color, position, etcetera, as we 
discussed in earlier lectures? 

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Does it transform nominal, interval, and 
quantitative information properly? 

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And does it use labels and legends 
appropriately?