There are two basic ways to present quantitative information – in a chart or in a table. We often focus on the proper design of charts, probably because they are the more engaging of the two presentation formats. However, table design is a fascinating subject that deals with how we can quickly perceive structure and differences between nominal values. One of the tenets of table design is to use redundancy with intent, and eliminate redundancy that simply clutters the message. For example, consider the table below, taken from this article on performance enhancements in Mac OS X:
Along with some relatively obvious flaws like color intensity selection, alignment, labeling, and fill, one of the biggest problems with this table is that it contains a large amount of redundant information. In the tables above, I count about 140 distinct pieces of information (words or numbers). Many of these words are repeated – things like the test names, the phrase “Standard Graphics Speed Tests”, etc. Besides presenting more information than is necessary for the reader, it obfuscates the design of the experiment. For example, it isn’t obvious that the two trials used similar tests (Cinebench 11.5, Xbench 1.3) until the reader actually reads all of the test names. Similarly, it isn’t obvious where the differences lie either – the (2x) and (8x) for Cinebench rendering are hidden behind redundant text, making them easy to miss and false assumptions to be made (e.g. the Mac Pro and the Macbook Pro both used 8x Cinebench rendering).
Consider the redesign below. I was able to reduce the number of pieces of information to ~95, a 33% reduction from the original table design. I eliminated as much redundancy as I could and used spatial proximity and a logical table structure to reveal the design of the experiment – two different computers, many similar tests. A reader can ascertain all of this information at a glance instead of reading through gobs of text. I also took one liberty with the data — I highlighted “significant” changes as opposed to those that appeared to be just noise, drawing the reader’s attention to the tests that showed the most change.
When designing a table, remember that the very structure of the table is a tool that you can use to convey organization, whether it be an experiment design, parent-child relationships (notice the indented lines), or similarity. Eliminate redundancy when it obfuscates these messages, and use it to highlight important messages (notice the redundant color selection between the title of the chart and the column labels).
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