blog-mining andrew_gelman_stats knowledge-graph by maker-knowledge-mining
the latest blogs:
Introduction: EJ points me to this new techno-thriller . Based on the sentence quoted above, I don’t see it selling lots of copies. It reads like a really boring Raymond Chandler. I still think these two movie ideas would be a better sell.
2 andrew gelman stats-2014-06-11-Bayes in the research conversation
Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
3 andrew gelman stats-2014-06-10-Spring forward, fall back, drop dead?
Introduction: Antonio Rinaldi points me to a press release describing a recent paper by Amneet Sandhu, Milan Seth, and Hitinder Gurm, where I got the above graphs (sorry about the resolution, that’s the best I could do). Here’s the press release: Data from the largest study of its kind in the U.S. reveal a 25 percent jump in the number of heart attacks occurring the Monday after we “spring forward” compared to other Mondays during the year – a trend that remained even after accounting for seasonal variations in these events. But the study showed the opposite effect is also true. Researchers found a 21 percent drop in the number of heart attacks on the Tuesday after returning to standard time in the fall when we gain an hour back. Rinaldi thinks: “On Tuesday? No multiple comparisons here???” The press release continues: “What’s interesting is that the total number of heart attacks didn’t change the week after daylight saving time,” said Amneet Sandhu, M.D., cardiology fellow, Univer
4 andrew gelman stats-2014-06-09-On deck this week
Introduction: Mon: I hate polynomials Tues: Spring forward, fall back, drop dead? Wed: Bayes in the research conversation Thurs: The health policy innovation center: how best to move from pilot studies to large-scale practice? Fri: Stroopy names Sat: He’s not so great in math but wants to do statistics and machine learning Sun: Comparing the full model to the partial model
5 andrew gelman stats-2014-06-09-I hate polynomials
Introduction: A recent discussion with Mark Palko [scroll down to the comments at this link ] reminds me that I think that polynomials are way way overrated, and I think a lot of damage has arisen from the old-time approach of introducing polynomial functions as a canonical example of linear regressions ( for example ). There are very few settings I can think of where it makes sense to fit a general polynomial of degree higher than 2. I think that millions of students have been brainwashed into thinking of these as the canonical functions and that this has caused endless trouble later on. I’m not sure how I’d change the high school math curriculum to deal with this, but I do think it’s an issue.
6 andrew gelman stats-2014-06-08-Regression and causality and variable ordering
Introduction: Bill Harris wrote in with a question: David Hogg points out in one of his general articles on data modeling that regression assumptions require one to put the variable with the highest variance in the ‘y’ position and the variable you know best (lowest variance) in the ‘x’ position. As he points out, others speak of independent and dependent variables, as if causality determined the form of a regression formula. In a quick scan of ARM and BDA, I don’t see clear advice, but I do see the use of ‘independent’ and ‘dependent.’ I recently did a model over data in which we know the ‘effect’ pretty well (we measure it), while we know the ’cause’ less well (it’s estimated by people who only need to get it approximately correct). A model of the form ’cause ~ effect’ fit visually much better than one of the form ‘effect ~ cause’, but interpreting it seems challenging. For a simplistic example, let the effect be energy use in a building for cooling (E), and let the cause be outdoor ai
7 andrew gelman stats-2014-06-07-“Does researching casual marijuana use cause brain abnormalities?”
Introduction: David Austin points me to a wonderfully-titled post by Lior Pachter criticizing a recent paper on the purported effects of cannabis use. Not the paper criticized here . Someone should send this all to David Brooks. I’ve heard he’s interested in the latest scientific findings, and I know he’s interested in marijuana.
8 andrew gelman stats-2014-06-06-Statistically savvy journalism
Introduction: Roy Mendelssohn points me to this excellent bit of statistics reporting by Matt Novak. I have no comment, I just think it’s good to see this sort of high-quality Felix Salmon-style statistically savvy journalism.
9 andrew gelman stats-2014-06-06-Hurricanes vs. Himmicanes
Introduction: The story’s on the sister blog and I quote liberally from Jeremy Freese, who wrote : The authors have issued a statement that argues against some criticisms of their study that others have offered. These are irrelevant to the above observations, as I [Freese] am taking everything about the measurement and model specification at their word–my starting point is the model that fully replicates the analyses that they themselves published. A qualification is that one of their comments is that they deny they are making any claims about the importance of other factors that kill people in hurricanes. But they are. If you claim that 27 out of the 42 deaths in Hurricane Eloise would have been prevented if it was named Hurricane Charley, that is indeed a claim that diminishes the potential importance of other causes of deaths in that hurricane. Freese also raises an important general issue in science communication: The authors’ university issued a press release with a dramatic prese
Introduction: Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write : We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We found that invasion by non-native plants was lowest in sites where the physical disturbance caused by flooding was both intense and frequent. . . . only species adapted to the dominant disturbance regimes at a site may become successful invaders. Their keywords are: causal networks; community dynamics; functional traits, invasive species, kettlehole; megafauna; rabbits; restoration; turf plants But here’s the part that I like best: We fitted all our models within a hierarchical Bayesian framework using . . . STAN v.1.3 (Stan Development Team 2012) from R v.2.15 (R Development Core Team 2012).