Fan of the blog, John H., made a JunkCharts-style post about a chart that has been picked as a "Best of" for 2014 by Fast Company (link). I agree with him. It seems more fit to be on the "Worst...
Fan of the blog, John H., made a JunkCharts-style post about a chart that has been picked as a "Best of" for 2014 by Fast Company (link). I agree with him. It seems more fit to be on the "Worst...
Alberto Cairo, Stephen McDaniel and I were asked about our "favorite" data visualization at the Qlik Conference this week. Stephen wrote up our answers here.
Nice article in the New York Times about the "overdiagnosis" problem in cancer screening. The particular case is thyroid cancer in South Korea. There are a number of things about any form of screening tests that one should always bear in mind: Death rate is measured as the number of deaths divided by the number of people with the disease. The latter number increases with better diagnosis techniques. Better diagnosis…
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…
Alberto Cairo discussed a graphic by the New York Times on the slowing growth of Medicare spending (link). The chart on the top is published, depicting the quite dramatic flattening of the growth in average spending over the last years--average...
Alberto links to a nice Propublica chart on average annual spend per dialysis patient on ambulances by state. (link to chart and article) It's a nice small-multiples setup with two tabs, one showing the states in order of descending spend...
Bernard L. tipped us about this "infographic": The chart is missing a title. The arcs present "sleep schedules" for the named people. The "data" comes from a book. I wonder about the accuracy of such data. Also note the inherent...
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…
As others binge watch Netflix TV, I binge read Gelman posts, while riding a train with no wifi and a dying laptop battery. (This entry was written two weeks ago.) Andrew Gelman is statistics’ most prolific blogger. Gelman-binging has become a necessity since I have not managed to keep up with his accelerated posting schedule. Earlier this year, he began publishing previews of future posts, one week in advance, and…
To add to my prior post, having now read the published paper on the effect of DST on heart attacks, I can confirm that I disagree with the way the publicist hired by the journal messaged the research conclusion. And some of the fault lies with the researchers themselves who appear to have encouraged the exaggerated claim. Here is the summary of the research as written up by the researchers…
