During the Washington D.C. water crisis, the Center for Disease Control (CDC) conducted a study purportedly to examine if there was any harm done to the public between 2001 and 2004. The study concluded that no harm occurred. After listening and talking to Dr. Simoni Triantafyllidou about her work on the same study objective as the CDC study, it becomes readily apparent that data manipulation is a key factor in the visualization of study outcomes. The CDC study lumped all blood lead levels (BLL) into a single data category whereas Dr. Triantafyllidou isolated the BLLs by high risk and low risks populations. This simple adjustment to the data set yielded significantly different visual results. By combining the data set, CDC softened the curve and generated like results as previous years. Their conclusion that this like result demonstrated no risk is just bad analysis but it wasn’t obvious from just looking at their graphic. An increased risk was demonstrated by the CDC study because they showed the Washington D.C. data as a shift above the national trend line. Dr. Triantafyllidou adjusted the picture to amplify that shift and wrote a different story line.
Edward Tufte has written several books on the visual display of quantitative information. Graphics reveal data and are instruments of communication and in this way, “data graphics are no different from words, [and] any means of communication can be used to deceive” (Tufte, 2001, 53). Tufte points out that when people are presented with visual information, they, “quickly and naturally” direct their attention, “toward exploring the substantive content of the data rather than toward questions of methodology and technique” (ibid., 20). “Our visual impression of the data is entangled,” in the ideologies of the producer and consumer of the graphic (ibid.).
In the same way a scientist or an engineer can harness authority just by their stated profession, a scientific graphic can harness authority because it looks like a fact. In the spirit of increased community participation in science, a sharedness of meaning must occur. How can complexity become more accessible? Most scientific or engineering graphics do not do this well. “Imagine if graphics were replaced by paragraphs of words and those paragraphs scattered over the pages out of sequence with the rest of the text [or meaning] – that is how graphical and tabular information is now treated in the layout of many published pages, particularly in scientific journals and professional books” (ibid., 181).
Tufte recommends some techniques in graphic display which might help. Words, graphics, and pictures should be combined in the display of information. Segregation of meaning between the words and the graphics should always be avoided. Specifically, in graphics in exploratory data analysis (as in BLL over time box charts), “words should tell the viewer how to read the design and not what to read in terms of content” (ibid., 182). The art and creativity of science lies in taking the data or facts and determining a finding. And citizens impacted by the risk a scientist or engineer may be studying, have a right to see the data and draw their own conclusions.
Tufte, Edward R. (2001). The Visual Display of Quantitative Information. Cheshire, CN: Graphics Press.