This year the idea that statistics is important for big data has exploded into the popular media. Here are a few examples, starting with the Lazer et. al paper in Science that got the ball rolling on this idea. The parable of Google Flu: traps in big data analysis Big data are we making a big mistake? Google Flu Trends: the limits of big data Eight (No, Nine!) Problems with Big Data All of these articles warn about issues that statisticians have been thinking about for a very long time: sampling populations, confounders, multiple testing, bias, and overfitting. In the rush to take advantage of the hype around big data, these ideas were ignored or not given sufficient attention.
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