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FCW : September 15, 2013
In Fung s view, the core problem is not that the creators of a dataset are trying to mislead --- though there are plenty of examples of that as well, many of which he has documented over the years on his "Junk Charts" blog. Rather, he says, most consumers of data are essentially innumerate and do not understand basic statistics or the countless judgment calls that go into developing a dataset. To ll those knowledge gaps, "Numbersense" presents eight chapter-length case studies. The consumer price index and monthly unemployment reports are placed under Fung s microscope, as are law school rankings, Groupon s eco- nomics, fantasy football stats and multiple rms marketing efforts. Even the dieter s dreaded body mass index gets deconstructed. So although Fung praises the Bureau of Labor Statistics for the "impressive accuracy" of its payroll survey, he shows how the de ni- tion of unemployment is at least as important as the tallying process. When does an out-of-work indi- vidual slip out of the workforce? Do you have any idea what the "seasonal adjustment" entails? And what happens when an employer simply skips that month s survey? As Fung notes, "Statisticians have a cautionary saying: Absence of evi- dence is not evidence of absence." At its core, Fung s warning boils down to Mark Twain s frequent dictum that there are three kinds of lies: lies, damned lies and statis- tics. Yet a basic understanding of data and some healthy skepticism can go a long way, Fung promises. Know where the numbers come from and what assumptions were made in crunching them, and you ll avoid the lion s share of confusion and mischief. As Fung succinctly puts it, "The key isn t how much data is ana- lyzed, but how." ■ Bad theory cannot be saved by data. Worse, bad theory and bad data analysis form a combustible mix. 32 September 15, 2013 FCW.COM What data do you have? What do you see and does it make sense? What visualization methods should you use? What do you want to know about your data? Best data ever (Speci c question) Explore different dimension Find related data Bar chart Pie chart Treemap Line plot Scatterplot New questions arise Don't know yet None or next to nothing A data visualization owchart Bookshelf START HERE Source: "Data Points"
August 30, 2013
September 30, 2013