andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2264 knowledge-graph by maker-knowledge-mining
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Introduction: Actually, more like the next month and a half . . . I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? Discrimination or calibration? Beyond the Valley of the Trolls Science tells us that fast food lovers are more likely to marry other fast food lovers References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling Adjudicating between alternative interpretations of a statistical interaction? The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? As the boldest experiment in journalism history, you admit you made a mistake Personally, I’d rather go with Teragram Bizarre academic spam An old discussion of food deserts Skepticism about a published cl
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1 I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? [sent-4, score-0.706]
2 The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? [sent-7, score-0.084]
3 This analogy is not perfect—unlike religions, statistical methods have no supernatural content and make essentially no demands on our personal lives. [sent-9, score-0.213]
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same-blog 1 0.99999988 2264 andrew gelman stats-2014-03-24-On deck this month
Introduction: Actually, more like the next month and a half . . . I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? Discrimination or calibration? Beyond the Valley of the Trolls Science tells us that fast food lovers are more likely to marry other fast food lovers References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling Adjudicating between alternative interpretations of a statistical interaction? The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? As the boldest experiment in journalism history, you admit you made a mistake Personally, I’d rather go with Teragram Bizarre academic spam An old discussion of food deserts Skepticism about a published cl
2 0.42215258 2310 andrew gelman stats-2014-04-28-On deck this week
Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
3 0.37705636 2298 andrew gelman stats-2014-04-21-On deck this week
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
4 0.37703344 2285 andrew gelman stats-2014-04-07-On deck this week
Introduction: Mon : How literature is like statistical reasoning: Kosara on stories. Gelman and Basbøll on stories. Tues : Understanding Simpson’s paradox using a graph Wed : Advice: positive-sum, zero-sum, or negative-sum Thurs : Small multiples of lineplots > maps (ok, not always, but yes in this case) Fri : “More research from the lunatic fringe” Sat : “Schools of statistical thoughts are sometimes jokingly likened to religions. This analogy is not perfect—unlike religions, statistical methods have no supernatural content and make essentially no demands on our personal lives. Looking at the comparison from the other direction, it is possible to be agnostic, atheistic, or simply live one’s life without religion, but it is not really possible to do statistics without some philosophy.” Sun : I was wrong . . .
5 0.34996921 2265 andrew gelman stats-2014-03-24-On deck this week
Introduction: OK, I’ve given up on theme weeks . I have enough saved-up material to do it, and it wouldn’t be too much trouble to group the scheduled posts into themes, but there doesn’t really seem to be a point. I say this because, having looked at the comment threads from the past few weeks, the comments seem pretty much tied to individual posts in any case. So I think I’ll go back to the old system where each post stands alone. Just for fun I thought I’d run a week’s worth of old posts, just some things I came across when searching for various things. Of course I could just post the links right here but instead I’ll repost with my comments on how things have changed in the intervening years. Mon : Empirical implications of Empirical Implications of Theoretical Models Tues : A statistical graphics course and statistical graphics advice Wed : What property is important in a risk prediction model? Discrimination or calibration? Thurs : Beyond the Valley of the Trolls Fri :
6 0.24881706 2320 andrew gelman stats-2014-05-05-On deck this month
7 0.22400112 2276 andrew gelman stats-2014-03-31-On deck this week
8 0.15816242 2324 andrew gelman stats-2014-05-07-Once more on nonparametric measures of mutual information
9 0.14061368 2290 andrew gelman stats-2014-04-14-On deck this week
11 0.10684478 1369 andrew gelman stats-2012-06-06-Your conclusion is only as good as your data
13 0.10624889 2314 andrew gelman stats-2014-05-01-Heller, Heller, and Gorfine on univariate and multivariate information measures
14 0.10355987 2366 andrew gelman stats-2014-06-09-On deck this week
15 0.10025612 2300 andrew gelman stats-2014-04-21-Ticket to Baaaath
16 0.099603429 98 andrew gelman stats-2010-06-19-Further thoughts on happiness and life satisfaction research
17 0.097090848 2263 andrew gelman stats-2014-03-24-Empirical implications of Empirical Implications of Theoretical Models
19 0.092960775 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
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same-blog 1 0.93751103 2264 andrew gelman stats-2014-03-24-On deck this month
Introduction: Actually, more like the next month and a half . . . I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? Discrimination or calibration? Beyond the Valley of the Trolls Science tells us that fast food lovers are more likely to marry other fast food lovers References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling Adjudicating between alternative interpretations of a statistical interaction? The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? As the boldest experiment in journalism history, you admit you made a mistake Personally, I’d rather go with Teragram Bizarre academic spam An old discussion of food deserts Skepticism about a published cl
2 0.90176523 2310 andrew gelman stats-2014-04-28-On deck this week
Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
3 0.85484493 2285 andrew gelman stats-2014-04-07-On deck this week
Introduction: Mon : How literature is like statistical reasoning: Kosara on stories. Gelman and Basbøll on stories. Tues : Understanding Simpson’s paradox using a graph Wed : Advice: positive-sum, zero-sum, or negative-sum Thurs : Small multiples of lineplots > maps (ok, not always, but yes in this case) Fri : “More research from the lunatic fringe” Sat : “Schools of statistical thoughts are sometimes jokingly likened to religions. This analogy is not perfect—unlike religions, statistical methods have no supernatural content and make essentially no demands on our personal lives. Looking at the comparison from the other direction, it is possible to be agnostic, atheistic, or simply live one’s life without religion, but it is not really possible to do statistics without some philosophy.” Sun : I was wrong . . .
4 0.84953284 2265 andrew gelman stats-2014-03-24-On deck this week
Introduction: OK, I’ve given up on theme weeks . I have enough saved-up material to do it, and it wouldn’t be too much trouble to group the scheduled posts into themes, but there doesn’t really seem to be a point. I say this because, having looked at the comment threads from the past few weeks, the comments seem pretty much tied to individual posts in any case. So I think I’ll go back to the old system where each post stands alone. Just for fun I thought I’d run a week’s worth of old posts, just some things I came across when searching for various things. Of course I could just post the links right here but instead I’ll repost with my comments on how things have changed in the intervening years. Mon : Empirical implications of Empirical Implications of Theoretical Models Tues : A statistical graphics course and statistical graphics advice Wed : What property is important in a risk prediction model? Discrimination or calibration? Thurs : Beyond the Valley of the Trolls Fri :
5 0.84010762 2298 andrew gelman stats-2014-04-21-On deck this week
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
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8 0.76124358 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
9 0.74695957 2253 andrew gelman stats-2014-03-17-On deck this week: Revisitings
10 0.74327666 2206 andrew gelman stats-2014-02-10-On deck this week
11 0.72800195 2348 andrew gelman stats-2014-05-26-On deck this week
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14 0.68121451 2339 andrew gelman stats-2014-05-19-On deck this week
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18 0.61614567 2222 andrew gelman stats-2014-02-24-On deck this week
19 0.60913795 679 andrew gelman stats-2011-04-25-My talk at Stanford on Tuesday
20 0.55389309 2320 andrew gelman stats-2014-05-05-On deck this month
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same-blog 1 0.95981085 2264 andrew gelman stats-2014-03-24-On deck this month
Introduction: Actually, more like the next month and a half . . . I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? Discrimination or calibration? Beyond the Valley of the Trolls Science tells us that fast food lovers are more likely to marry other fast food lovers References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling Adjudicating between alternative interpretations of a statistical interaction? The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? As the boldest experiment in journalism history, you admit you made a mistake Personally, I’d rather go with Teragram Bizarre academic spam An old discussion of food deserts Skepticism about a published cl
2 0.94222939 1675 andrew gelman stats-2013-01-15-“10 Things You Need to Know About Causal Effects”
Introduction: Macartan Humphreys pointed me to this excellent guide . Here are the 10 items: 1. A causal claim is a statement about what didn’t happen. 2. There is a fundamental problem of causal inference. 3. You can estimate average causal effects even if you cannot observe any individual causal effects. 4. If you know that, on average, A causes B and that B causes C, this does not mean that you know that A causes C. 5. The counterfactual model is all about contribution, not attribution. 6. X can cause Y even if there is no “causal path” connecting X and Y. 7. Correlation is not causation. 8. X can cause Y even if X is not a necessary condition or a sufficient condition for Y. 9. Estimating average causal effects does not require that treatment and control groups are identical. 10. There is no causation without manipulation. The article follows with crisp discussions of each point. My favorite is item #6, not because it’s the most important but because it brings in some real s
3 0.94160068 810 andrew gelman stats-2011-07-20-Adding more information can make the variance go up (depending on your model)
Introduction: Andy McKenzie writes: In their March 9 “ counterpoint ” in nature biotech to the prospect that we should try to integrate more sources of data in clinical practice (see “ point ” arguing for this), Isaac Kohane and David Margulies claim that, “Finally, how much better is our new knowledge than older knowledge? When is the incremental benefit of a genomic variant(s) or gene expression profile relative to a family history or classic histopathology insufficient and when does it add rather than subtract variance?” Perhaps I am mistaken (thus this email), but it seems that this claim runs contra to the definition of conditional probability. That is, if you have a hierarchical model, and the family history / classical histopathology already suggests a parameter estimate with some variance, how could the new genomic info possibly increase the variance of that parameter estimate? Surely the question is how much variance the new genomic info reduces and whether it therefore justifies t
4 0.93899864 514 andrew gelman stats-2011-01-13-News coverage of statistical issues…how did I do?
Introduction: This post is by Phil Price. A reporter once told me that the worst-kept secret of journalism is that every story has errors. And it’s true that just about every time I know about something first-hand, the news stories about it have some mistakes. Reporters aren’t subject-matter experts, they have limited time, and they generally can’t keep revisiting the things they are saying and checking them for accuracy. Many of us have published papers with errors — my most recent paper has an incorrect figure — and that’s after working on them carefully for weeks! One way that reporters can try to get things right is by quoting experts. Even then, there are problems with taking quotes out of context, or with making poor choices about what material to include or exclude, or, of course, with making a poor selection of experts. Yesterday, I was interviewed by an NPR reporter about the risks of breathing radon (a naturally occurring radioactive gas): who should test for it, how dangerous
5 0.93808651 1401 andrew gelman stats-2012-06-30-David Hogg on statistics
Introduction: Data analysis recipes: Fitting a model to data : We go through the many considerations involved in fitting a model to data, using as an example the fit of a straight line to a set of points in a two-dimensional plane. Standard weighted least-squares fitting is only appropriate when there is a dimension along which the data points have negligible uncertainties, and another along which all the uncertainties can be described by Gaussians of known variance; these conditions are rarely met in practice. We consider cases of general, heterogeneous, and arbitrarily covariant two-dimensional uncertainties, and situations in which there are bad data (large outliers), unknown uncertainties, and unknown but expected intrinsic scatter in the linear relationship being fit. Above all we emphasize the importance of having a “generative model” for the data, even an approximate one. Once there is a generative model, the subsequent fitting is non-arbitrary because the model permits direct computation
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19 0.91424906 1453 andrew gelman stats-2012-08-10-Quotes from me!