Posts Tagged ‘ science ’

Pondering OCCAM data in medicine

October 9, 2014
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Pondering OCCAM data in medicine

The New York Times Magazine has a pretty good piece about the use of OCCAM data to solve medical questions, like diagnosis and drug selection. I'm happy that it paints a balanced picture of both the promise and the pitfalls. Here are some thoughts in my head as I read this piece: Small samples coupled with small effects pose a design problem in traditional clinical trials. The subjects of the…

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Experts clinging on to their predictions

October 7, 2014
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Andrew Gelman touches on one of my favorite topics: prediction accuracy, and experts who cling to their predictions. Here's Andrew at the Monkey Cage blog. His starting point is a piece by sociologist Jay Livingston on how various well-known economists made vague predictions (e.g. "I see inflation around the corner") and kept clinging to them (eventually, there will be inflation). Several theories are given to explain this behavior. One is…

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A patently pointless picture

October 3, 2014
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A patently pointless picture

I am mystified by the intention behind this chart, published in NYT Magazine (Sept 14, 2014). It is not a data visualization since the circles were not placed to scale. The 650 and 660 should have been further to the...

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Princeton’s loss of nerve

October 2, 2014
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Princeton’s loss of nerve

I have earlier reported that Princeton's new President has initiated a review of their "grade deflation" policy that was put in almost ten years ago. As you may recall (link), grading in U.S. colleges has become a farce: at top-tier schools, getting an A means you are an average student; not getting an A is many times more informative than getting an A. The new administration at Princeton has now…

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You don’t have all the data

September 9, 2014
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Binging on Gelman again while I was traveling. Here is a gem (link). If you ever dreamed of having "all the data", be sure to give this a read and give it a few moments' thought. Skim through the main post quickly. The good stuff is in the comments.

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Davenport on targeted marketing

August 13, 2014
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Tom Davenport is one of the leading voices on business analytics, and he has a new piece titled "Why are most 'targeted' marketing offers so bad?" in which he expanded on a question I raised in my HBR article. Tom's book Competing on Analytics is a classic. He has a great appreciation for the business of the data business. In the new feature, Davenport classifies marketing offers he gets into…

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A report from #JSM Joint Statistical Meetings 2014

August 6, 2014
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Stephen Stigler, the preeminent historian of statistics, gave a great talk at JSM, the annual gathering of statisticians on Monday afternoon in Boston. He outlined seven core ideas ("pillars of wisdom") in statistical research that sets the field apart; these are ideas developed by statisticians that represent significant advances to science and to human knowledge. As he remarked, each of these advances overturned then-established science, but even today, many people…

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Around the blogosphere

July 10, 2014
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A number of folks have reacted to various blogs and talks I have recently given. I'm glad that my writing has inspired others, and I recommend reading these wonderful responses. *** Diane Ravitch, the eminent scholar of New York education and author of several great books, found my 2011 post about Bill Gates's view of education. Here is her reaction: How refreshing to know that statisticians like Kaiser Fung are…

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The Facebook experiment controversy

July 1, 2014
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Facebook data scientists are being blasted for a social psychology experiment they ran in 2012 in which they varied the amount of positive/negative content exposed to users in newsfeeds and measured whether this affected the positive/negative content posted by those users. (link to WSJ report; link to paper) I'm perplexed by the reaction. Boing Boing's Cory Doctorow calls it "likely illegal", who links to James Grimmelmann, a law professor. Slate…

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How to read Big Data studies

June 25, 2014
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This is part 3 of my response to Gelman's post about the DST/heart attacks study. The previous parts are here and here. One of the keys of vetting any Big Data/OCCAM study is taking note of the decisions made by the researchers in conducting the analysis. Most of these decisions involve subjective adjustments or unverifiable assumptions. Not that either of those things are inherently bad - indeed, any analysis one…

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