It's hard to keep up with Andrew Gelman, so let me point to some interesting recent posts from his blog.
Readings on philosophy of statistics (link): Andrew has a bunch of links of (mostly his own) writings about deep statistical issues. Science is about understanding how the world works, which involves questions of cause and effect, and randomness and unexplained variability. Data that can be observed are almost never sufficient to establish cause decisively but statistical theories can be drawn upon to make careful, principled conjectures. These statistical methods are not infallible, and are subject to abuses, both malign and unintentional. Recent work has uncovered that lots of results from all kinds of fields (psychology, social psychology, evolutionary psychology, medicine, cancer studies, etc.) cannot be replicated, raising concerns about abuses. Andrew - as well as commentators - compile a list of readings for those interested in this ongoing controversy.
An elementary error showing up in JAMA (link): misinterpreting p-values - every elementary textbook warns against such erroneous claims
Something for bird lovers (link) and for cat lovers (link)
Another one on Gelman's favorite subject - the garden of forking paths leading to over-confident statistical conclusions. I once summarized his arguments in a series of posts: 1, 2, 3
Some commentary on Mechanical Turk and the general issue of measurement and data quality (link) This is an important topic in Big Data. I will be writing about a study that looks at weather as the explanatory variable. Weather is derived by looking up someone's IP address and then the weather report at that IP address. One should ask how accurate the measurement of weather was for this study.
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