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# Sure-fire ways to spoil data, Part I

In his bestseller “The Situation Is Hopeless, but Not Serious”, Paul Watzlawick describes how people can pursue unhappiness (or remain unhappy if they already are). In a similar yet unrelated topic, Howard Wainer explains how people can make consumers of charts miserable as well. According to the American statistician, anyone can succeed in spoiling data by following a few, simple guidelines:

 Show as few data as possible (i.e. Minimize the data density). If the chart looks too empty because it only contains a handful of values, fill the rest with pretty pictures that do nothing to help further explain the facts. Hide what data you do show. And there are many effective ways to do so. Use a flashy grid and print the data on it – but in a subdued color. Alternatively, you can firmly abide to the “never chop the axes” rule so that the interesting differences among the data are barely visible. Ignore the visual metaphor altogether. Do not sort data by size even if the chart type and data allow it. For example, use a bar chart but sort the data in alphabetical order. Only order matters. Use length as a criterion for organizing objects, but confuse readers by showing them as an area. This way, you can magnify even the smallest of differences. Graph data out of context. This works well with time-series analyses. If you want to disguise a strong drop of a value, simply start the chart with the following period. If the difference is minimal, just enlarge the scale and compress the X axis. Change scales in mid-axis. Simply take two rows of data, scale them individually and place each of them into a different chart. Then combine those charts to one – something for connoisseurs.