A colleague who knows my fascination with data visualization pointed me to a recent interesting video created by Geoff McGhee on Journalism in the Age of Data. In this 8-part video, he interviews media people who create visualizations for their websites at the New York Times, Washington Post, CNBC, and more. It is interesting to see their view of why interactive visualization might be useful to their audience, and how it is linked to "good journalism".
Also interviewed are a few visualization interface developers (e.g., IBM's Many Eyes designers) as well as Infographics experts and participants at the major Inforgraphics conference in Pamplona, Spain. The line between beautiful visualizations (art) and effective ones is discussed in Part IV ("too sexy for its own good" - Gert Nielsen) - see also John Grimwade's article.
Journalism in the Age of Data from Geoff McGhee on Vimeo.
The videos can be downloaded as a series of 8 podcasts, for those with narrower bandwidth.
Showing posts with label WSJ. Show all posts
Showing posts with label WSJ. Show all posts
Sunday, November 14, 2010
Friday, April 20, 2007
Statistics are not always the blame!
My current MBA student Brenda Martineau showed me a March 15, 2007 article in the Wall Street Journal entitled Stupid Cancer Statistics. Makes you almost think that once again someone is abusing statistics -- but wait! A closer look reveals that the real culprit is not the "mathematical models", but rather the variable that is being measured and analyzed!
According to the article, the main fault is in measuring (and modeling) mortality rate in order to determine the usefulness of breast cancer early screening. Women who get diagnosed early (before the cancer escapes the lung) do not necessarily live longer than those who do not get diagnosed. But their quality of life is much improved. Therefore, the author explaines, the real measure should be quality of life. If I understand this correctly, this really has nothing to do with "faulty statistics", but rather with the choice of measurement to analyze!
In short, although a popular habit, you can't always blame all statistical models all the time...
According to the article, the main fault is in measuring (and modeling) mortality rate in order to determine the usefulness of breast cancer early screening. Women who get diagnosed early (before the cancer escapes the lung) do not necessarily live longer than those who do not get diagnosed. But their quality of life is much improved. Therefore, the author explaines, the real measure should be quality of life. If I understand this correctly, this really has nothing to do with "faulty statistics", but rather with the choice of measurement to analyze!
In short, although a popular habit, you can't always blame all statistical models all the time...
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