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]]>Rembrandt in a walk:

-He believes that “God is in every leaf on every tree”. Most of his greatest paintings are portraits of himself or regular people (as opposed to portraits of kings or Popes, or mythical battles, or etc.) Same for his etchings.

-He believes in embracing variation. Check out especially his later work, which is famously unpolished and is all the more evocative for it. In contrast, Russell spent his whole career trying, and failing, to impose more precision on the foundations of mathematics and language than is possible.

-As a painter, he knows a thing or two about the importance of one’s “model”.

Normally I’d go for any comment that points to my obsessions, but this comment by Jeremy is so clearly doing so, that I’ll have to disqualify it. Also, he didn’t mention Stan. So I’m calling this one for Updike.

Now for today’s bout. I don’t know enough Carlins for that to be an entire category, but George made it in the Comedians category, and he’s up against conceptual artist Barbara Kruger.

If it was up to my friends from high school, Harvey would go for George, and Kenny would go for Barbara. But it’s up to you. Whaddya think?

My first thought is that Carlin should win easily—but, there’s just one thing. Many years ago when I was sick and home from school, I turned on a daytime TV talk show and, who should I see but George Carlin! He was doing a set that was perfectly adapted to his audience. I don’t remember the details but it was things like: Y’know how, when you’re in the supermarket, the cart just spins and spins around? etc. He was doing bits about shopping and doing the laundry and whatever else he thought would work with that audience. What was weird about it was that it was so clearly non-Carlin material, yet it was given the standard Carlin delivery.

At some level this is admirable professionalism—but it also struck me as a bit creepy, almost as if someone released a video of Newt Gingrich giving a stirring soak-the-rich speech to the American Socialists organization, or, umm, I dunno, seeing Ed Wegman give a lecture on research integrity. Put it this way: After seeing his performance on that talk show, I have no doubt that Carlin could give a set that’s perfectly adapted to the Columbia audience—but would we care?

Say what you want about Barbara Kruger—call her a talentless self-promoter with a one-note shtick, whatever—at least you have to admit she won’t compromise.

P.S. As always, here’s the background, and here are the rules.

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]]>The post One simple trick to make Stan run faster appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>source(“http://mc-stan.org/rstan/stan.R”)

It’s from Stan core developer Ben Goodrich.

This simple line of code has changed my life. A factor-of-4 speedup might not sound like much, but, believe me, it is!

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]]>The post Introducing shinyStan appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>

As a project for Andrew’s Statistical Communication and Graphics graduate course at Columbia, a few of us (Michael Andreae, Yuanjun Gao, Dongying Song, and I) had the goal of giving RStan’s print and plot functions a makeover. We ended up getting a bit carried away and instead we designed a graphical user interface for interactively exploring virtually any Bayesian model fit using a Markov chain Monte Carlo algorithm.

The result is shinyStan, a package for R and an app powered by Shiny. The full version of shinyStan v1.0.0 can be downloaded as an R package from the Stan Development Team GitHub page here, and we have a demo up online here. If you’re not an R user, we’re working on a full online version of shinyStan too.

For me, there are two primary motivations behind shinyStan:

- Immediate, informative, customizable visual and numerical summaries of model parameters and convergence diagnostics for MCMC simulations.
- Good defaults with many opportunities for customization.

- Store the basic components of an entire project (code, posterior samples, graphs, tables, notes) in a single object.
- Export graphics into R session as ggplot2 objects for further customization and easy integration in reports or post-processing for publication.

There’s also a third thing that has me excited at the moment. That online demo I mentioned above… well, since you’ll be able to upload your own data soon enough and even add your own plots if we haven’t included something you want, imagine an interactive library of your models hosted online. I’m imagining something like this except, you know, finite, useful, and for statistical models instead of books. (Quite possibly with fewer paradoxes too.) So it won’t be anything like Borges’ library, but I couldn’t resist the chance to give him a shout-out.

Finally, for those of you who haven’t converted to Stan quite yet, shinyStan is agnostic when it comes to inputs, which is to say that you don’t need to use Stan to use shinyStan (though we like it when you do). If you’re a Jags or Bugs user, or if you write your own MCMC algorithms, as long as you have some simulations in an array, matrix, mcmc.list, etc., you can take advantage of shinyStan.

If you haven’t stopped reading yet and want a more detailed list of features, release notes are below. But why read the notes when you can try it out right now! And if you do try it out, we’d love your feedback.

shinyStan v1.0.0 ====================================================================== Interactive and customizable plots: ---------------------------------------- * Parameter estimates * Traceplots for individual or multiple parameters * Dynamic trace plots for individual parameters with dygraphs (JavaScript) charting library * Autocorrelation for individual or multiple parameters * Bivariate scatterplots * (New) Trivariate scatter plots (using three.js library) * (New) Distributions of Rhat, effective sample size / total sample size, monte carlo error / posterior sd Customizable tables (via jQuery DataTables) ---------------------------------------- * Posterior summary statistics (can now search table with regular expressions for easier filtering) * Average, max and min of sampler parameters (for NUTS and HMC algorithms) Other features: ---------------------------------------- * In addition to stanfit objects, you can also use arrays of simulations and mcmc.lists with shinyStan * Model code is viewable in the shinyStan GUI * Save notes about your model * Save plots as ggplot2 objects (i.e. not just the image but an object that can be edited with functions from the ggplot2 package) * Glossaries with definitions of terms used in the tables * Generate new quantities as a function of one or two existing parameters Coming soon to your local shinyStan: ---------------------------------------- * Graphical posterior predictive checks * shinyStan online * Deploy a shinyStan app for each of your models online to shinyapps.io (Start an online library of your models) * Add your own custom plots to a shinystan objects so you can really store everything in one object

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]]>The post Rembrandt van Rijn (2) vs. Bertrand Russell appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Rabbit Angstrom is a perfect example of the life that the Buddha warns against. He is a creature of animal passions who never gains any enlightenment.

In any case, I think we can all agree that Buddha is a far more interesting person than Updike. But, following the rules of the contest, we’re going with the best comment, which comes from Ethan:

Updike. We could ask him to talk to the title “Stan fans spark Bayes craze.” Buddha might just meditate silently for the whole hour.

Bonus points for bringing in Stan *and* baseball.

Today, the ultimate Dutch master is up against the ultimate rationalist. Rembrandt will paint the portrait of anyone who doesn’t paint himself.

I gotta say, this is one rough pairing. Who wouldn’t want to see Rembrandt do a quick painting demonstration? But, Russell must have been a great lecturer, witty and deep and he could even do math! I have a feeling that Rembrandt was a nicer guy (it would hard to *not* be a nicer guy than Bertrand Russell, right?), but I don’t know how relevant that is in choosing a speaker.

P.S. As always, here’s the background, and here are the rules.

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]]>The post What hypothesis testing is all about. (Hint: It’s not what you think.) appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>I’ve said it before but it’s worth saying again.

**The conventional view:**

Hyp testing is all about rejection. The idea is that if you reject the null hyp at the 5% level, you have a win, you have learned that a certain null model is false and science has progressed, either in the glamorous “scientific revolution” sense that you’ve rejected a central pillar of science-as-we-know-it and are forcing a radical re-evaluation of how we think about the world (those are the accomplishments of Kepler, Curie, Einstein, and . . . Daryl Bem), or in the more usual “normal science” sense in which a statistically significant finding is a small brick in the grand cathedral of science (or a stall in the scientific bazaar, whatever, I don’t give a damn what you call it), a three-yards-and-a-cloud-of-dust, all-in-a-day’s-work kind of thing, a “necessary murder” as Auden notoriously put it (and for which was slammed by Orwell, a lesser poet put a greater political scientist), a small bit of solid knowledge in our otherwise uncertain world.

But (to continue the conventional view) often our tests don’t reject. When a test does not reject, *don’t* count this as “accepting” the null hyp; rather, you just don’t have the power to reject. You need a bigger study, or more precise measurements, or whatever.

**My view:**

My view is (nearly) the opposite of the conventional view. The conventional view is that you can learn from a rejection but not from a non-rejection. I say the opposite: you can’t learn much from a rejection, but a non-rejection tells you something.

A rejection is, like, ok, fine, maybe you’ve found something, maybe not, maybe you’ll have to join Bem, Kanazawa, and the Psychological Science crew in the “yeah, right” corner—and, if you’re lucky, you’ll understand the “power = .06″ point and not get so excited about the noise you’ve been staring at. Maybe not, maybe you’ve found something real—but, if so, you’re not learning it from the p-value or from the hypothesis tests.

A non-rejection, though: this tells you something. It tells you that your study is noisy, that you don’t have enough information in your study to identify what you care about—even if the study is done perfectly, even if measurements are unbiased and your sample is representative of your population, etc. That can be some useful knowledge, it means you’re off the hook trying to explain some pattern that might just be noise.

It doesn’t mean your theory is wrong—maybe subliminal smiley faces really *do* “punch a hole in democratic theory” by having a big influence on political attitudes; maybe people really *do* react different to himmicanes than to hurricanes; maybe people really do prefer the smell of people with similar political ideologies. Indeed, any of these theories could have been true even before the studies were conducted on these topics—and there’s nothing wrong with doing some research to understand a hypothesis better. My point here is that the large standard errors tell us that these theories are not well tested by these studies; the measurements (speaking very generally of an entire study as a measuring instrument) are too crude for their intended purposes. That’s fine, it can motivate future research.

Anyway, my point is that standard errors, statistical significance, confidence intervals, and hypotheses tests are far from useless. In many settings they can give us a clue that our measurements are too noisy to learn much from. That’s a good thing to know. A key part of science is to learn what we *don’t* know.

Hey, kids: Embrace variation and accept uncertainty.

**P.S.** I just remembered an example that demonstrates this point, it’s in chapter 2 of ARM and is briefly summarized on page 70 of this paper.

In that example (looking at possible election fraud), a rejection of the null hypothesis would *not* imply fraud, not at all. But we do learn from the *non*-rejection of the null hyp; we learn that there’s no evidence for fraud in the particular data pattern being questioned.

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]]>The post On deck this week appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Rembrandt van Rijn (2) vs. Betrand Russell

**Tues:** One simple trick to make Stan run faster

George Carlin (2) vs. Barbara Kruger

**Wed:** I actually think this infographic is ok

Bernard-Henry Levy (3) vs. Jacques Derrida

**Thurs:** Defaults, once set, are hard to change.

Judy Garland (4) vs. Al Sharpton

**Fri:** “The Saturated Fat Studies: Set Up to Fail”

John Waters (1) vs. Bono

**Sat:** “With that assurance, a scientist can report his or her work to the public, and the public can trust the work.”

Plato (1) vs. Mark Twain (4)

**Sun:** Causal Impact from Google

Mary Baker Eddy vs. Mohammad (2)

No “On deck this month” this month because I don’t know what all the seminar-speaker matchups are gonna be. I’ll tell you, though, we have some excellent posts in the regular series. So stay tuned!

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]]>The post Buddha (3) vs. John Updike appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Now for today’s battle. Buddha is seeded #3 among founders of religions. Updike is the unseeded author of the classic Rabbit, Run, and dozens of memorable short stories, but is detested by Helen DeWitt and various commenters on this blog.

Who’d be a better speaker? Updike is more of a Harvard guy but I guess he’d give a talk at Columbia if we asked, right?

P.S. As always, here’s the background, and here are the rules.

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]]>The post “Precise Answers to the Wrong Questions” appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Can we get a mathematical framework for applying statistics that better facilitates communication with non-statisticians as well as helps statisticians avoid getting “precise answers to the wrong questions*”?

Applying statistics involves communicating with non-statisticians so that we grasp their applied problems and they understand how the methods we propose address our (incomplete) grasp of their problems. Statistical theory on the other hand, involves communicating with oneself and other qualified statisticians about statistical models that embody theoretical abstractions and one would be foolish to limit mathematical approaches in this task. However, as put in Kass, R. (2011), Statistical Inference: The Big Picture – “Statistical procedures are abstractly defined in terms of mathematics but are used, in conjunction with scientific models and methods, to explain observable phenomena. … When we use a statistical model to make a statistical inference [address applied problems] we implicitly assert … the theoretical world corresponds reasonably well to the real world.” Drawing on clever constructions by Francis Galton and insights into science and mathematical reasoning by C.S. Peirce, this talk will discuss an arguably mathematical framework (in the Peirce’s sense of diagrammatic reasoning) that might be better.

*“An approximate answer to the right question is worth a great deal more than a precise answer to the wrong question.” – John Tukey.

**P.S.** from Andrew: Here’s my article from 2011, Bayesian Statistical Pragmatism, a discussion of Rob Kass’s article on statistical pragmatism.

Key quote from my article:

In the Neyman–Pearson theory of inference, confidence and statistical significance are two sides of the same coin, with a confidence interval being the set of parameter values not rejected by a significance test. Unfortunately, this approach falls apart (or, at the very least, is extremely difficult) in problems with high-dimensional parameter spaces that are characteristic of my own applied work in social science and environmental health.

In a modern Bayesian approach, confidence intervals and hypothesis testing are both important

but are not isomorphic[emphasis added]; they represent two different steps of inference. Confidence statements, or posterior intervals, are summaries of inference about parameters conditional on an assumed model. Hypothesis testing—or, more generally, model checking—is the process of comparing observed data to replications under the model if it were true.

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]]>The post Friedrich Nietzsche (4) vs. Alan Bennett appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>But the funniest argument came from Jonathan:

As near as I can figure, Shakespeare was nothing more than a guy who could string a bunch of famous phrases together and make a play out of them. It’s a talent, to be sure, but a fairly minor one. Plus, if he’s in love with Gwyneth Paltrow, I’m out.

Ouch! Willie got zinged, so Karl’s in.

And, today it’s “God is dead” vs. the ultimate cozy comedian. Amazingly enough, it’s been more than 40 years since the first performance of “Forty Years On.”

P.S. As always, here’s the background, and here are the rules.

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]]>The post Bertrand Russell goes to the IRB appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Here’s a fun one for those of you still based at a university.

All of you put together a Human Ethics Review proposal for a field experiment on Human Ethics Review proposals.

Here is the proposal within my proposal.

Each of you would propose putting together a panel of researchers at different universities. You would propose that each of your panel members – from diverse fields, seniority levels, ethnicities and such – would submit a proposal to his or her ethics review board or Institutional Review Board for approval, and each of the panellists would track the time it took to get the proposal approved, which legitimate ethical issues were flagged, which red herring issues also held things up, and how long and onerous the whole ordeal was.

Still in your proposal, you would then propose gathering the data from your panellists and drawing some conclusions about what sorts of schools have better or worse processes. Specific hypotheses to be tested would be whether universities with medical schools were worse than others because medical ethicists would be on the panel, and whether universities with faculty-based rather than centralised IRBs would have better approval processes.

You would note that members of your panels could ask their University’s HR advisers to get data on the people who are on the IRBs – race, gender, ethnicity, area of study, rank, age, experience, time on panel, number of children, marital status, and sexual orientation (though not all of those would be in each place’s HR database); you’d propose using these as control variables but also to test whether a panel’s experience made any difference and whether having a panel member from your home Department made any difference. It would also be interesting to note whether the gender, seniority, ethnicity and home department of the submitter made any difference to the application.

End of the proposal-within-the-proposal.

Now for the fun part: each one of you reading this is a potential member of a panel for a study for which nobody has ever sought ethical approval, but which will be self-approving in a particularly distributed fashion: The IRB proposal to be tested is the one I’ve just outlined. Whichever of you first gets ethical approval is the lead author on the paper, is a data point, and already has the necessary ethics approval. Everybody else, successful or not, is a data point.

This is just the greatest. You can only do this sort of study if you have IRB approval, but the only way to get IRB approval is . . . to do the study!

This is related to other paradoxes such as: I can do nice (or mean) things to people and write about what happens, but call it “research” and all of a sudden we’re in big trouble if we don’t get permission. Crampton’s idea is beautiful because it wraps the problem in itself. Russell, Cantor, and Godel would be proud.

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]]>The post William Shakespeare (1) vs. Karl Marx appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Popper. We would learn more from falsifying the hypothesis that Popper’s talk is boring than what we would learn from falsifying the hypothesis that Richard Pryor’s talk is uninteresting.

And today we have the consensus choice for greatest writer vs. the notorious political philosopher. Marx is unseeded in the Founders of Religions category but he’s had lots of influence on the world. Both these guys are pretty quotable. So who’s it gonna be, the actor or the radical?

P.S. No Groucho jokes, please. And no need for reminders that lots of bad things were done in the name of Marxism. We’re choosing a seminar speaker here, that’s all. We’re not endorsing a philosophy.

P.P.S. As always, here’s the background, and here are the rules.

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]]>The post “The harm done by tests of significance” (article from 1994 in the journal, “Accident Analysis and Prevention”) appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>In your January 2013 Commentary (Epidemiology) you say that “…misunderstanding persists even in high-stakes settings.” Attached is an older paper illustrating some such.

“It is like trying to sink a battleship by firing lead shot at it for a long time”—well put!

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]]>The post Richard Pryor (1) vs. Karl Popper appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>The top-seeded comedian vs. an unseeded philosopher. Pryor would be much more entertaining, that’s for sure (“Arizona State Penitentiary population: 80 percent black people. But there are no black people in Arizona!”). But Karl Popper laid out the philosophy that is the foundation for modern science. His talk, even if it is dry, might ultimately be more interesting.

What do you think?

P.S. As always, here’s the background, and here are the rules.

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]]>The post Psych journal bans significance tests; stat blogger inundated with emails appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>From Brandon Nakawaki:

I know your blog is perpetually backlogged by a few months, but I thought I’d forward this to you in case it hadn’t hit your inbox yet. A journal called Basic and Applied Social Psychology is banning null hypothesis significance testing in favor of descriptive statistics. They also express some skepticism of Bayesian approaches, but are not taking any action for or against it at this time (though the editor appears opposed to the use of noninformative priors).

From Joseph Bulbulia:

I wonder what you think about the BASP’s decision to ban “all vestiges of NHSTP (P-values, t-values, F-values, statements about “significant” differences or lack thereof and so on)”?

As a corrective to the current state of affairs in psychology, I’m all for bold moves. And the emphasis on descriptive statistics seems reasonable enough — even if more emphasis could have placed on visualising the data, more warnings could have been issued around the perils of un-modelled data, and more value could have been placed on obtaining quality data (as well as quantity).

My major concern, though, centres on the author’s timidness about Bayesian data analysis. Sure, not every Bayesian analysis deserves to count as a contribution, but nor is it the case that Bayesian methods should be displaced while descriptive methods are given centre stage. We learn by subjecting our beliefs to evidence. Bayesian modelling merely systematises this basic principle, so that adjustments to belief/doubt are explicit.

From Alex Volfovsky:

I just saw this editorial from Basic and Applied Social Psychology: http://www.tandfonline.com/doi/pdf/10.1080/01973533.2015.1012991

Seems to be a somewhat harsh take on the question though gets at the frequently arbitrary choice of “p<.05" being important...

From Jeremy Fox:

Psychology journal bans inferential statistics: As best I can tell, they seem to have decided that all statistical inferences from sample to population are inappropriate.

From Michael Grosskopf:

I thought you might find this interesting if you hadn’t seen it yet. I imagine it is mostly the case of a small journal trying to make a name for itself (I know nothing of the journal offhand), but still is interesting.

http://www.tandfonline.com/doi/pdf/10.1080/01973533.2015.1012991

From the Reddit comments on a thread that led me to the article:

“They don’t want frequentist approaches because you don’t get a posterior, and they don’t want Bayesian approaches because you don’t actually know the prior.”http://www.reddit.com/r/statistics/comments/2wy414/social_psychology_journal_bans_null_hypothesis/

From John Transue:

Null Hypothesis Testing BANNED from Psychology Journal: This will be interesting.

From Dominik Papies:

I assume that you are aware of this news, but just in case you haven’t heard, one journal from psychology issued a ban on NHST (see editorial, attached). While I think that this is a bold move that may shake things up nicely, I feel that they may be overshooting, as not the technique per se, but rather its use seems the real problem to me. The editors also state they will put more emphasis on sample size and effect size, which sounds like good news.

From Zach Weller:

One of my fellow graduate students pointed me to this article (posted below) in the Basic and Applied Social Psychology (BASP) journal. The article announces that hypothesis testing is now banned from BASP because the procedure is “invalid”. Unfortunately, this has caused my colleague’s students to lose motivation for learning statistics. . . .

From Amy Cohen:

From the Basic and Applied Social Psychology editorial this month:

The Basic and Applied Social Psychology (BASP) 2014 Editorial emphasized that the null hypothesis significance testing procedure (NHSTP) is invalid, and thus authors would be not required to perform it (Trafimow, 2014). However, to allow authors a grace period, the Editorial stopped short of actually banning the NHSTP. The purpose of the present Editorial is to announce that the grace period is over. From now on, BASP is banning the NHSTP. With the banning of the NHSTP from BASP, what are the implications for authors?

From Daljit Dhadwal:

You may already have seen this, but I thought you could blog about this: the journal “Basic and Applied Social Psychology” is banning most types of inferential statistics (p-values, confidence intervals, etc.).

Here’s the link to the editorial:

http://www.tandfonline.com/doi/full/10.1080/01973533.2015.1012991

John Kruschke blogged about it as well:

http://doingbayesiandataanalysis.blogspot.ca/2015/02/journal-bans-null-hypothesis.html

The comments on Kruschke’s blog are interesting too.

OK, ok, I’ll take a look. The editorial article in question is by David Trafimow and Michael Marks. Krushke points out this quote from the piece:

The usual problem with Bayesian procedures is that they depend on some sort of Laplacian assumption to generate numbers where none exist. The Laplacian assumption is that when in a state of ignorance, the research should assign an equal probability to each possibility.

Huh? This seems a bit odd to me, given that I just about always work on continuous problems, so that the “possibilities” can’t be counted and it is meaningless to talk about assigning probabilities to each of them. And the bit about “generating numbers where none exist” seems to reflect a misunderstanding of the distinction between a *distribution* (which reflects uncertainty) and *data* (which are specific). You don’t want to deterministically impute numbers where the data don’t exist, but it’s ok to assign a distribution to reflect your uncertainty about such numbers. It’s what we always do when we do forecasting; the only thing special about Bayesian analysis is that it applies the principles of forecasting to all unknowns in a problem.

I was amused to see that, when they were looking for an example where Bayesian inference is OK, they used a book by R. A. Fisher!

Trafimow and Marks conclude:

Some might view the NHSTP [null hypothesis significance testing procedure] ban as indicating that it will be easier to publish in BASP [Basic and Applied Social Psychology], or that less rigorous manuscripts will be acceptable. This is not so. On the contrary, we believe that the p < .05 bar is too easy to pass and sometimes serves as an excuse for lower quality research. We hope and anticipate that banning the NHSTP will have the effect of increasing the quality of submitted manuscripts by liberating authors from the stultified structure of NHSTP thinking thereby eliminating an important obstacle to creative thinking.

I’m with them on that. Actually, I think standard errors, p-values, and confidence intervals can be very helpful in research when considered as convenient parts of a data analysis (see chapter 2 of ARM for some examples). Standard errors etc. are helpful in giving a lower bound on uncertainty. The problem comes when they’re considered as the culmination of the analysis, as if “p less than .05″ represents some kind of proof of something. I do like the idea of requiring that research claims stand on their own without requiring the (often spurious) support of p-values.

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]]>The post Abraham (4) vs. Jane Austen appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Aristotle, though, I could care less.

But the commenters mostly favored Aristotle, basically on the grounds that he invented science. And, as Keith put it, “being scientific is absolutely no defense again being wrong, but rather just an acceleration of the process of getting less wrong.” And Aristotle is probably a good seminar speaker—seminars are what they did all day back then, right?

Ultimately I’ll have to go with Patrick:

Stewart Lee.

I can’t see Aristotle presenting a seminar on his biggest philosophical mistakes.

But I can see Lee spending a seminar on his least funny jokes, and getting a few laughs at the same time.

And today’s match is a forfeit. Abraham (listed as #4 in the Founders of Religions category) does not belong in this contest. The other 63 people in the bracket are real people, Abraham is the only fictional character here, he just doesn’t belong. So Jane will advance, uncontested, to the next round.

P.S. As always, here’s the background, and here are the rules.

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]]>The post The axes are labeled but I don’t know what the dots represent. appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>John Sukup writes:

I came across a chart recently posted by Boston Consulting Group on LinkedIn and wondered what your take on it was. To me, it seems to fall into the “suspicious” category but thought you may have a different opinion.

I replied that this one baffles me cos I don’t know what the dots represent! This is typical in graphs, the axes are labeled but I don’t know what it is that is being labeled.

Sukup replied:

Indeed. The axes are also labeled strangely and the years used to calculate the CAGR are also not the same. I’d have expected more from BCG—but this type of data visualization error seems pervasive these days!

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]]>The post Aristotle (3) vs. Stewart Lee appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Some arguments in the comments in favor of Freud: From Huw, “he has the smirks, knowing looks, and barely missed sidelong glances.” And Seth points out the statistical connection: “Some people might say that theory is getting lost in the identification revolution. Freud didn’t have that problem.” And Manoel picks up on an old line from this blog and writes that “I think we should pick Freud as the typical economist . . . which are under-represented in this contest. Arguably, both have a silly theory of human action and a huge impact on our society nonetheless.”

In favor of King, Zbicyclist recommends stopping Freud’s ideas spreading farther, and he add: “this sets up a possible Gandhi vs King match two rounds further.” That’s a good argument but I’ll have to go with Freud, because he inspired so much more enthusiasm, positive and negative, in the comment thread.

And, today, the third-best philosopher vs. the 41st Best Stand-up Ever.

I don’t know what to think about Aristotle. On one hand, he invented science. On the other, he’s most famous for being wrong. Whether the topic is slavery, or the laws of motion, or how many teeth are in the mouth of men and women—you name it, Aristotle’s on the wrong end of the stick.

On the other hand, if he truly is an empiricist, Aristotle might give a good talk in which he re-evaluates his philosophy in response to learning about all these famous errors.

Stewart Lee is more of a known quantity. You can check out his DVD’s.

P.S. As always, here’s the background, and here are the rules.

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]]>The post Upcoming Stan-related talks appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>

**New York**

- 25 February. Jonah Gabry:
*shinyStan: a graphical user interface for exploring Bayesian models after MCMC*. Register Now: New York Open Statistical Programming Meetup. - 12 March. Rob Trangucci:
*#5: Non-centered parameterization aka the “Matt trick.”*Register Now: Stan Users NYC Meetup.

**Sydney**

- 4 March. Bob Carpenter:
*The Benefits of a Probabilistic Model of Data Annotation*. Macquarie Uni Computer Science Dept. - 10 March, 2–3 PM. Bob Carpenter:
*Stan: Bayesian Inference Made Easy*. Macquarie Uni Statistics Dept. Building E4A, room 523. - 11 March, 6 PM, Mithcell Theatre, Level 1 at SMSA (Sydney Mechanics’ School of Arts). Bob Carpenter:
*RStan: Bayesian Inference Made Easy.*Register Now: Sydney Users of R Forum (SURF) (Meetup)

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]]>The post “A small but growing collection of studies suggest X” . . . huh? appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>I’m curious if you have any thoughts on the statistical meaning of sentences like “a small but growing collection of studies suggest [X].” That exact wording comes from this piece in the New Yorker, but I think it’s the sort of expression you often see in science journalism (“small but mounting”, “small but growing”, etc.). A post on your own blog quotes a New York Times piece using the phrase, “a growing body of science suggesting [X]” but the post does not address the expression itself.

For Bayesians the weight of evidence available now should be all that matters, right? How the weight of evidence has changed with respect to time would seem to offer no additional information. If anything, trends in research should themselves be based on the evidence already revealed, so it seems like double-counting to include growth-in-evidence as evidence itself.

Maybe there is a more complicated justification. For example, if researchers have both unpublished evidence and (weak) published evidence and their research agenda is determined by both, then the very fact that they the number of such studies is “growing” more quickly than would seem to be justified by the (weak) published evidence could itself be an indicator that the unpublished evidence bolsters the (weak) published evidence. That seems way too convoluted to be what the journalist or reader could have had in mind, though!

So I’m curious whether you think “growing evidence” is a statistical howler? There are over 700,000 google hits for the phrase “growing evidence,” so if it really means nothing, that will be news to a lot of writers and editors.

Interesting question. How would we model this process? Sometimes it does seem to happen that a new hypothesis arises and the evidence becomes stronger and stronger in its favor (for example, global warming); other times there’s a new hypothesis and the evidence just doesn’t seem to be there (for example, cold fusion). Still other times the evidence seems to simmer along at a sort of low boil, with a continuing supply of evidence but nothing completely convincing (for example, stereotype threat). Ultimately, though we like to think of the evidence as increasing toward one conclusion or another.

So, maybe the phrase “growing evidence” is ok. But this only works if we accept that sometimes the evidence *isn’t* growing.

To see this, shift away from the press and go into the lab. It is natural to take inconclusive evidence and think of it as the first step on the road to success. Suppose, for example, you have some data and you get an estimate of 2.0 with a standard error of 1.4. This is not statistically significant—but it’s close! And it’s easy to think that, if you just double your sample size, you’ll get success: double your sample size, the standard error goes down by a factor of sqrt(2), and you get a standard error of 1.0: the estimate will be 2 standard errors away from 0. But that’s incorrect because there’s no reason to assume that the estimate will stay fixed at 2.0. Indeed, under the prior in which small effects are more likely than large effects, it’s more likely the estimate will go lower rather than higher, once more data come in.

So, in that sense, I agree with Lee Beck that the frame of “small and growing evidence” can be misleading, in that it encourages a mode of thinking in which we first extrapolate from what we see, then we implicitly condition on these potential data that haven’t occurred yet, in order to make our conclusions stronger than they should be.

And then you end up with renowned biologist James D. Watson saying in 1998, “Judah is going to cure cancer in two years.” There was a small but mounting pile of evidence.

It’s 2015. Judah did a lot of things in his time, but cancer is still here.

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]]>The post Martin Luther King (2) vs. Sigmund Freud appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>And today we have the second seed among the Religious Leaders vs. an unseeded entry in the Founders of Religions category. Truly a classic matchup. MLK perhaps has the edge here because he can talk about plagiarism; on the other hand, Freud is an expert in unfalsifiable research theories. I imagine that either one would be an amazingly compelling speaker. King would have a lot to say about Middle East wars, globalization, and economic and social inequality; and Freud could wittily diagnose all of society’s problems. I’d love to have them both—but that’s not allowed. So who’s it gonna be?

P.S. As always, here’s the background, and here are the rules.

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]]>The post “Unbiasedness”: You keep using that word. I do not think it means what you think it means. [My talk tomorrow in the Princeton economics department] appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>The talk is tomorrow, Tues 24 Feb, 2:40-4:00pm in 200 Fisher Hall:

“Unbiasedness”: You keep using that word. I do not think it means what you think it means.

Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

Minimizing bias is the traditional first goal of econometrics. In many cases, though, the goal of unbiasedness can lead to extreme claims that are both substantively implausible and not supported by data. We illustrate with several examples in areas ranging from public opinion to social psychology to public heath, using methods including regression discontinuity, hierarchical models, interactions in regression, and data aggregation. Methods that purport to be unbiased, aren’t, once we carefully consider inferential goals and select on the analyses that are actually performed and reported. The implication for econometrics research: It’s best to be aware of all sources of error, rather than to focus narrowly on reducing bias with respect to one particular aspect of your model.

This work reflects collaboration with Guido Imbens and others. Here are the slides, and people can read the following papers for partial background:

Why high-order polynomials should not be used in regression discontinuity designs. (Andrew Gelman and Guido Imbens)

[2015] Evidence on the deleterious impact of sustained use of polynomial regression on causal inference. Research and Politics. (Andrew Gelman and Adam Zelizer)

[2014] Beyond power calculations: Assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science 9, 641-651. (Andrew Gelman and John Carlin)

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]]>The post On deck this week appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Martin Luther King (2) vs. Sigmund Freud

**Tues:** “A small but growing collection of studies suggest X” . . . huh?

Aristotle (3) vs. Stewart Lee

**Wed:** The axes are labeled but I don’t know what the dots represent.

Abraham (4) vs. Jane Austen

**Thurs:** In criticism of criticism of criticism

Richard Pryor (1) vs. Karl Popper

**Fri:** “The harm done by tests of significance” (article from 1994 in the journal, “Accident Analysis and Prevention”)

William Shakespeare (1) vs. Karl Marx

**Sat:** Forget about pdf: this looks much better, it makes all my own papers look like kids’ crayon drawings by comparison

Friedrich Nietzsche (4) vs. Alan Bennett

**Sun:** Time-release pedagogy??

Buddha (3) vs. John Updike

The post Philip K. Dick (2) vs. Jean Baudrillard appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>In addition to his unique painting style and very special life, van Gogh was highly literate, as shown through the 844 letters from him that are available today.

X also made a missing-body-part joke, which I generally don’t think is so cool but, if anyone’s allowed to get away with that sort of humor, it’s X.

Anyway, now I was curious so I googled *Vincent Van Gogh letters* and found this site. I clicked through and looked at a few letters and they seemed like nothing special.

So, given that this was the best argument in favor and it wasn’t so great, I’ll have to call it for Grandma Moses, boring as she sounds.

Today we have Horselover Fat vs. the self-parodying intellectual. Dick would seem to be the easy winner here. But Baudrillard did write this:

Decidedly, joggers are the true Latter Day Saints and the protagonists of an easy-does-it Apocalypse. Nothing evokes the end of the world more than a man running straight ahead on a beach, swathed in the sounds of his walkman, cocooned in the solitary sacrifice of his energy, indifferent even to catastrophes since he expects destruction to come only as the fruit of his own efforts, from exhausting the energy of a body that has in his own eyesbecome useless. Primitives, when in despair, would commit suicide by swimming out to sea until they could swim no longer. The jogger commits suicide by running up and down the beach. His eyes are wild, saliva drips from his mouth. Do not stop him. He will either hit you or simply carry on dancing around in front of you like a man possessed.

I think we can safely say this is a contest between two guys who did not spend much time at the gym.

P.S. As always, here’s the background, and here are the rules.

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]]>The post “Academics should be made accountable for exaggerations in press releases about their own work” appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>For anyone with medical training, mainstream media coverage of science can be an uncomfortable read. It is common to find correlational findings misrepresented as denoting causation, for example, or findings in animal studies confidently exaggerated to make claims about treatment for humans. But who is responsible for these misrepresentations?

In the linked paper (doi:10.1136/bmj.g7015) Sumner and colleagues found that much of the exaggeration in mainstream media coverage of health research—statements that went beyond findings in the academic paper—was already present in the press release sent out to journalists by the academic institution itself.

Sumner and colleagues identified all 462 press releases on health research from 20 leading UK universities over one year. They traced 668 associated news stories . . .

The story is pretty much as you’d predict: a lot of the exaggeration comes in the press release.

I remarked that this makes sense. I agree. Of course, this is just a start, as I’m sure a lot of academics would be happy to put their names on various exaggerated claims! See, for example, here, where the researchers in question were very active with the publicity, and in which they dramatically overstated the implications on individual-level behavior that could be drawn from their state-level analysis. The lead research in this case was just a law professor, but still, we’d like to see better.

As this example illustrates, the problem is not necessarily any sort of conscious exaggeration or hype: I assume that the researchers in question really believe that their claims are supported by their data. For that matter, I assume that disgraced primatologist Mark Hauser really believes his theories.

To put it another reason: be skeptical of press releases, not because they’re written by sleazy public relations people, but because they’re written by, or with the collaboration, of researchers who know enough to make a superficially convincing case but not enough to recognize the flaws in their reasoning.

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]]>The post Vincent van Gogh (3) vs. Grandma Moses appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>I’ll have to with Mother Teresa. I doubt we’ll see any faith healings but I’m persuaded by Ken’s negative case against Moon:

Moon’s wikipedia page describes him as a “religious leader, businessperson, political activist, and media mogul.” In my experience people with “rock star” status like this make for bad seminar speakers because they tend to be full of anecdotes and fluff, and light on rigorous empirical evidence.

Good point. The last thing we need here is a goddam Ted talk.

And today we have a contest between two artists! Vincent is more famous and would certainly be the bigger draw, but I wouldn’t be surprised if Grandma could give a more coherent lecture. On the other hand, according to wikipedia, “she was a Society of Mayflower Descendants and Daughters of the American Revolution member.” And that sounds pretty duuuulllllllll. It’s up to all of you to make the strongest and wittiest arguments on both sides.

P.S. As always, here’s the background, and here are the rules.

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]]>The post Bayes and doomsday appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>I am a fellow Bayesian statistician at the University of New South Wales (Australia). I have enjoyed reading your various books and articles, and enjoyed reading your recent article on The Perceived Absurdity of Bayesian Inference. However, I disagree with your assertion that the “doomsday argument” is non-Bayesian; I think if you read how it is presented by Leslie you will see that it is at least

an attemptat a Bayesian argument. In any case, although it has enoughprima facieplausibility to trick people, the argument is badly flawed, and not a correct application of Bayesian reasoning. I don’t think it is a noose around the Bayesian neck.Anyway, I’m just writing because I thought you might be interested in a recent paper on this topic in the Journal of Philosophy. The paper is essentially a Bayesian refutation of the doomsday argument, pointing out how it goes wrong, and how it is an incorrect application of Bayesian inference. (And also how a correct application of Bayesian inference leads to sensible conclusions.) Essentially, the argument confuses total series length with remaining series length, and sneaks information from the data into the prior in a way which is invalid. Once this is corrected the absurd conclusions of the doomsday argument evaporate.

I don’t really have anything more to say on this topic (here’s my argument from 2005 as to why I think the doomsday argument is clearly frequentist and not particularly Bayesian) but I thought some of you might be interested, hence the pointer.

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]]>The post The bracket so far appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Our competition is (approximately) 1/4 done!

And I’ve been thinking about possible categories for next year’s tourney:

New Jersey politicians

Articulate athletes

Plagiarists

People named Greg or Gregg

Vladimir Nabokov and people connected to him

. . .

Ummm, we need 3 more categories. Any suggestions? Real people only, please. In some future year we can have an all-fictional category.

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]]>The post Mother Teresa (4) vs. Sun Myung Moon appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Jonathan argued in favor of Kubrick based on this quote: “Well, you don’t make it easy on viewers or critics. You’ve said you want an audience to react emotionally. You create strong feelings, but you won’t give us any easy answers. That’s because I don’t have any easy answers.”

But, as a Bayesian, I *do* want easy answers, so this line of argument doesn’t work for me.

And now for today we have two people who’ve done a lot, but might not be very articulate seminar speakers. The nun is seeded #4 in the Religious Leaders category; Sun Myung Moon is listed under Cult Figures. For sheer spectacle, ya gotta go with the Moonies. On the other hand, with Mother Teresa we might see a documented miracle. According to Wikipedia, “In 2002, the Vatican recognised as a miracle the healing of a tumor in the abdomen of an Indian woman, Monica Besra, after the application of a locket containing Mother Teresa’s picture.”

It’s been 13 years, so maybe Teresa is up for another miraculous cure?

P.S. As always, here’s the background, and here are the rules.

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]]>The post Statistical Significance – Significant Problem? appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>The post Statistical Significance – Significant Problem? appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>The post Mohandas Gandhi (1) vs. Stanley Kubrick appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Jesus spoke in parables to avoid committing to a specific point, like he was saying things without really saying them. In other words, he was deliberately ambiguous. If he were a modern day researcher, he would propose all this ambiguous theories, that can always come true with a bit of data and multiple comparisons. Jesus would publish in Psych Science.

So I’d resist. But, then, Jesus being who he is, I expect he’d charm the hell out of me. And, by the end, I’d probably be worshipping the guy. It sounds like a truly transformative experience that nothing secular could match. I mean, Jesus is the ultimate nice guy, right? But with a backbone of steel. Muscular Christianity and all that.

And his material is good, too. As Tom wrote in comments:

Jesus has by far the best encore of all time, so we know he must be a bit of a showman. Also – five loaves and two fishes will take care of the catering for the seminar. Dinner and a show – what more do you want?

All that and parables too. Take *that*, Larry David.

Buuuuuut—and this is a bit “but”—check out this comment from Dalton:

I don’t know. People keep trying to schedule a seminar with the dude, and he never shows:

http://en.wikipedia.org/wiki/List_of_dates_predicted_for_apocalyptic_events

That’s a big problem. It would be pretty embarrassing for us to announce this big seminar and then have the speaker not show up.

Who does he think he is? Lauryn Hill???

So, sorry, it’ll be Tolstoy who advances this time. Thanks, as always, for your thoughtful and humorous exchanges in the comments. This is a lot more fun than working, right?

Next, we have a truly titanic struggle. In one corner we have Mohandas Gandhi, seeded as the #1 Religious Leader of all time. (Remember, we have Founders of Religion in a different category.) And he’s up against Stanley Kubrick, unseeded in the Cult Figures category and also with a strong anti-war message. I can only imagine that Kubrick would have a much more entertaining series of stories. But Gandhi . . . what can you say? Talk about an impressive person. Maybe just being in the same room with the guy would be enough. What do *you* think?

P.S. As always, here’s the background, and here are the rules.

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]]>The post Another example of why centering predictors can be good idea appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Andrew Dolman writes:

Just in case you need another example of why it is important to consider what the intercepts in a model represent, here is a short comment I [Dolman] just got published correcting a misinterpretation of a simple linear model, that would not have happened if they had centered their predictor around a sensible value.

The actual topic here is “Macrophyte effects on algal turbidity in subtropical versus temperate lakes.” That is so cool! I google-imaged “algal turbidity” and found the above pretty picture, which is on a webpage with a full discussion of turbidity. Surprisingly interesting stuff.

Back to Dolman and interpreting the intercept: Here’s his graph:

You can perhaps already see what happened. The other researchers noticed that the two lines had essentially the same intercept, and they concluded that the two groups differed only in the slopes, not the level of the lines. But this was a mistake in the context of the data. It’s similar to the example that Jennifer and I give in our book with the regressions of earnings on height. The intercept of such a regression doesn’t have much meaning, as it corresponds to the earnings of someone with a height of zero inches.

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]]>The post Jesus (1) vs. Leo Tolstoy appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>And today we have another serendipitously appropriate pairing. The #1 Christian of all time vs. a successful landowner who, at the end of his life, devoted himself to Christianity. The teller of crisp parables vs. the author of a really long novel about a lady who throws herself under a train. Two white guys with beards.

We got some high-quality talent here in the lower-left corner of the bracket.

P.S. As always, here’s the background, and here are the rules.

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]]>The post VB-Stan: Black-box black-box variational Bayes appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>We describe an automatic variational inference method for approximating the posterior of differentiable probability models. Automatic means that the statistician only needs to define a model; the method forms a variational approximation, computes gradients using automatic differentiation and approximates expectations via Monte Carlo integration. Stochastic gradient ascent optimizes the variational objective function to a local maximum. We present an empirical study that applies hierarchical linear and logistic regression models to simulated and real data.

Alp clarifies:

This is not standard variational Bayes. The core differences are:

+ gaussian variational approximation in unconstrained parameter space

+ gradient-based algorithm. *not* coordinate ascent.

+ stochastic optimize. *not* the same as stochastic variational inference (SVI) where data is subsampled.

Our next goals are to test the current code on more complicated models and get started on implementing SVI. This will allow stan to address massive datasets, which piqued many people’s interest at NIPS 2014.

My contributions to this project so far have mostly been limited to expressing a lot of enthusiasm about the idea, making some connections between people, and supplying some data for one of the examples.

There’s a lot of good stuff here and I have high hopes for it. The code is open and available for all; I hope it will be in regular Stan soon so anybody can easily try it out.

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]]>The post Larry David (4) vs. Thomas Hobbes appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>And now, for today, we have a misanthropists’ version of yesterday’s contest: the grumpy comedian battling it out with the consummate realist political philosopher.

Nasty, brutish, and short, indeed. It’s a bit scary how appropriate this matchup is, considering they were all coming in at random.

Recently I’ve been irritated at Larry David because he’s been saturating the local print media with irritatingly-smug ads for his new Broadway show. But that shouldn’t really affect our decision. I think Hobbes would have a lot to say about various recent events in Ukraine and the Middle East. On the other side, one could argue that David’s insights into human nature are universal.

P.S. Please, no Calvin and Hobbes jokes. Thomas Hobbes is a great man on his own, and there is no point in comparing him to a cartoon animal who happens to be named after him.

P.P.S. As always, here’s the background, and here are the rules.

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]]>The post Bayesian survival analysis with horseshoe priors—in Stan! appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Tomi Peltola, Aki Havulinna, Veikko Salomaa, and Aki Vehtari write:

This paper describes an application of Bayesian linear survival regression . . . We compare the Gaussian, Laplace and horseshoe shrinkage priors, and find that the last has the best predictive performance and shrinks strong predictors less than the others. . . .

And here’s their R and Stan code!

P.S. Here’s more horseshoe from PyStan developer Allen Riddell, who writes:

Betting that only a subset of the explanatory variables are useful for prediction is a bet on sparsity. A popular model making this bet is the Lasso or, less handily, L1-regularized regression. A Bayesian competitor to the Lasso makes use of the “Horseshoe prior” (which I’ll call “the Horseshoe” for symmetry). This prior captures the belief that regression coefficients are rather likely to be zero (the bet on sparsity). The following shows how to use the Horseshoe in Stan.

And the Horseshoe+ prior from Anindya Bhadra, Jyotishka Datta, Nicholas Polson, and Brandon Willard, who write:

The horseshoe+ prior is a natural extension of the horseshoe prior . . . concentrates at a rate faster than that of the horseshoe in the Kullback-Leibler (K-L) sense . . . lower mean squared error . . . In simulations, the horseshoe+ estimator demonstrates superior performance in a standard design setting against competing methods, including the horseshoe and Dirichlet-Laplace estimators. . . .

And they too have R and Stan code!

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]]>The post Chris Rock (3) vs. Jean-Jacques Rousseau appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>My reasoning isn’t based on any single comment, but rather that there was a lot more passion in the comments about Beauvoir, pro and con. I think it’s fair to label Raymond Carver as a creature of the 1970s and Simone de Beauvoir as a resident of the 1950s—but Beauvoir’s issues just seem more alive, at least to the commenters of this blog. So the feminist advances past the alcoholic.

But what really interests me is today’s contest. Pookie vs. Emile.

Now we’re talking. “Bullet control” vs. the social contract. These are two heavy hitters. Rock would be more fun, that’s for sure, but Rousseau’s a living legend. Well, ok, he’s dead, but if he were alive he’d be a living legend, that’s for sure.

Give it your best shot.

P.S. As always, here’s the background, and here are the rules.

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]]>The post James Watson sez: Cancer cure is coming in minus 14 years! appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Within a year, if all goes well, the first cancer patient will be injected with two new drugs that can eradicate any type of cancer, with no obvious side effects and no drug resistance — in mice.

Some cancer researchers say the drugs are the most exciting treatment that they have ever seen. But then they temper their enthusiasm with caution, noting that the history of cancer treatments is full of high expectations followed by dashed hopes when drugs with remarkable effects in animals are tested in people.

Still, the National Cancer Institute has made the drugs its top priority, said Dr. Richard D. Klausner, the director. Dr. Klausner called them ”the single most exciting thing on the horizon” for the treatment of cancer.

The reporter is careful to mix enthusiasm with caution:

Dr. James M. Pluda, who is directing the cancer institute’s planned tests of the drugs in patients, said he and others at the institute were ”electrified” when they heard the drug’s discoverer deliver a lecture about the newest results. ”People were almost overwhelmed,” Dr. Pluda said. ”The data were remarkable.”

Although the discovery of the drugs, and some of their effects, have been reported over the past few years, Dr. Pluda said that ”if people understood how many steps ahead” the research was compared to what had been published, ”they’d be even more in awe.”

But Dr. Jerome Groopman, a cancer researcher at the Harvard Medical School, was wary. ”We are all driven by hope,” Dr. Groopman said. ”But a sober scientist waits for the data.” And until the drugs are given to humans, he said, the crucial data simply do not exist.

But here’s a quote from the biggest name in biology:

Other scientists are not so restrained. “Judah is going to cure cancer in two years,” said Dr. James D. Watson, a Nobel laureate who directs the Cold Spring Harbor Laboratory, a cancer research center on Long Island.

It’s good to remember that even famous people—even people who are famous for being smart—can say stupid things.

Watson does seem to have a talent for hyperbole, though. I bet he wrote really good letters of recommendations and really good grant applications. Those are two arenas, along with journalism, where there’s a clear incentive to hype hype hype.

**P.S.** We shouldn’t blame Watson for the cancer cure not working out. It’s probably the fault of all those Africans, women, and fat people in science. If only biology were a purer field, then I’m pretty sure we’d have had that cancer cure as of 2000, just on schedule. It’s totally unfair. Thin white guys do all the work, then people like Oprah come in and try to get all the credit. What’s with that, anyway???

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]]>Chris Rock (3) vs. Jean-Jacques Rousseau

**Tues:** Bayesian survival analysis with horseshoe priors—in Stan!

Larry David (4) vs. Thomas Hobbes

**Wed:** VB-Stan: Black-box black-box variational Bayes

Jesus (1) vs. Leo Tolstoy

**Thurs:** Another example of why centering predictors can be good idea

Mohandas Gandhi (1) vs. Stanley Kubrick

**Fri:** Statistical Significance – Significant Problem?

Mother Teresa (4) vs. Sun Myung Moon

**Sat:** Bayes and doomsday

Vincent van Gogh (3) vs. Grandma Moses

**Sun:** “Academics should be made accountable for exaggerations in press releases about their own work”

Philip K. Dick (2) vs. Jean Baudrillard

The post Stan Down Under appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>I (Bob, not Andrew) am in Australia until April 30. I’ll be giving some Stan-related and some data annotation talks, several of which have yet to be concretely scheduled. I’ll keep this page updated with what I’ll be up to. All of the talks other than summer school will be open to the public (the meetups will probably require registration).

- 19 February:
*Bayesian Inference and MCMC*. Machine Learning Summer School (NICTA). - 20 February:
*A Practical Introduction to Stan*. Machine Learning Summer School (NICTA). - 4 March, 11 AM — Noon.
*The Benefits of a Probabilistic Model of Data Annotation*. Macquarie Uni Computer Science Dept. - 5 March:
*Stan: A Probabilistic Programming Language*. NICTA Sydney (broadcast to other NICTA offices) - 10 March, 2–3 PM.
*Stan: A Probabilistic Programming Language*. Macquarie Uni Statistics Dept. Building E4A, room 523. - 11 March, 6 PM, Mitchell Theatre, Level 1 at SMSA (Sydney Mechanics’ School of Arts) :
*RStan: Bayesian Inference Made Easy.*Register Now: Sydney Users of R Forum (SURF) (Meetup)

- 9 April, 1 PM:
*Stan: Bayesian Inference Made Easy*. Burwood Corporate Centre, Level 2 Building BC, Deakin University, 221 Burwood Highway, Burwood. Room details available on the day by asking at reception. - TBD:
*A Probabilistic Model of Data Annotation*. Uni of Melbourne, Computer Science.

I’m also happy to get together one-on-one with people to discuss computation or modeling. If you’d like to get together, please e-mail me at carp@alias-i.com.

The data annotation talks will be based largely on a a paper with Becky Passonneau, where we use Mechanical Turk generated labels for the dictionary meaning intended by thousands of word instances in context; the data’s all open access as is the code to reproduce the paper.

I’ll extend the paper by talking about Bayesian approaches to hierarchical modeling (pushback from earlier referees led to us using penalized MLE in the paper), jointly estimating a model and gold standard, modeling item annotation difficulty, and a few other things, including a philosophical discussion of whether the truth is really out there. This is the modeling problem that made me realize I needed to learn Bayesian stats properly and led to my working with Andrew in the first place. He and Jennifer Hill helped me develop a basic latent gold-standard multinomial model to adjust for annotator inaccuracy and bias, though it turns out I was scooped by (Dawid and Skene 1979).

The Stan talks are, of course, based on Stan and RStan.

I just searched [stan] on Google from a fresh sim card in Sydney on google.com.au.

Good news: We’re ahead of Eminem.

Bad news: We’re behind the streaming media service (the Netflix of Australia). So far behind, we’re not even on the first page of hits (we were the first hit on page 2).

What happened? I thought Google had added results diversity so, for example, you don’t get 1000 hits for Michael Jordan the basketball player before the first hit for Michael Jordan the computer scientist (or maybe vice-versa these days?). Here’s the seminal paper on results diversity from my old colleague Jaime Carbonell and crew from way back in 1998. The basic idea is your next result should balance similarity to the query and difference from earlier results.

Of course, we could’ve followed Hadley’s adviced and called our system McMcWalla2 or something like that.

You can help boost our rankings — just link to mc-stan.org from your .au web page!

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]]>The post Simone de Beauvoir (2) vs. Raymond Carver appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Yoko. I’d go up to her after the seminar and give her a list of all the bands I hate, and ask her if she could break them up too.

Similarly from Daniel:

Alan Turing broke the Enigma code. Yoko Ono broke-up the Beatles. Both impressive, historic accomplishments, but Turning had a whole team of cryptography bosses working alongside him, while Yoko was going it alone.

But Jameson and other commenters pointed out that the whole blame-Yoko thing is a bit unfair. It could well be that Paul McCartney finally realized he was a genius and didn’t feel like playing second-banana to someone with less talent than he had.

Ultimately, I’ll have to go for Turing, based on Ethan’s comment, “I wonder what he’d make of Stan.”

And now for something completely different. The Second Sex vs. the author who has a quasi-official association with Alcoholics Anonymous.

What we talk about when we talk about feminism.

P.S. As always, here’s the background, and here are the rules.

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]]>The post “Peer assessment enhances student learning” appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Peer assessment has received attention lately as a way of providing personalized feedback that scales to large classes. . . . By conducting a randomized controlled trial in an introductory statistics class, we provide evidence that peer assessment causes significant gains in student achievement. The strength of our conclusions depends critically on the careful design of the experiment, which was made possible by a web-based platform that we developed. Hence, our study is also a proof of concept of the high-quality experiments that are possible with online tools.

Sun wrote to me:

We conducted a crossover study to see whether students who participated in peer assessment learned more than students who didn’t. In our new study, we took into account your suggestions about our first study, especially about principles of educational measurement. For one, we designed harder exams (somewhat to the chagrin of the students).

I have not looked at the paper in detail but, just speaking generally, I love this sort of study. Even if the end result is “no effect” or “no statistically significant effect,” I still think it’s important in that it pushes us to think harder about what we want our students to learn. As we discussed yesterday, measurement is super-important and is, I think, an underrated aspect of statistics.

I do have one suggestion: it’s a suggestion that’s pretty much universal in any study of this sort:

Make a scatterplot where each dot is a student, and you plot “after” score vs. “before” score, using different colored dots for treated and control students, and you can also draw the regression lines on the graph.

I find this sort of graph to be essential in the understanding of any study of this sort.

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]]>I’d rather watch him talk than watch one of his movies.

And, in all seriousness, I think Wood’s talk would be better. Schlafly must’ve given thousands of speeches by now, and I think whatever she has to say would just be boring. Either she’d go with her usual material or she’d try to rile up the liberal New York audience; either way, who cares? With Ed, though, anything could happen.

Some commenters wanted Schlafly on the grounds that it would be fun to see all the protestors. But that doesn’t really seem so interesting to me either. So Ed Wood it is, and he’ll be up against the winner of today’s matchup.

It’s Alan Turing (seeded #2 in the Philosophers category) vs. Yoko Ono (unseeded Religious Leader).

For a statistics audience, I guess Turing’s got this one pretty much locked up. I don’t really see how Yoko has a chance at all—she’s a bigger underdog than Buster Douglas. The codebreaker can save his energy for the next round.

P.S. As always, here’s the background, and here are the rules.

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]]>The post Two Unrecognized Hall Of Fame Statisticians appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Any suggestions? Dead people only, please.

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]]>The Modern French Intellectuals category doesn’t get a lot of love from this group. So it’s up to me to decide. Raymond Aron’s work is of more interest to me, but I’ll have to go with Levi-Strauss for the cool factor. From Wikipedia: “The Elementary Structures of Kinship . . . was even reviewed favorably by Simone de Beauvoir, who viewed it as an important statement of the position of women in non-western cultures. A play on the title of Durkheim’s famous Elementary Forms of the Religious Life, Elementary Structures re-examined how people organized their families by examining the logical structures that underlay relationships rather than their contents. While British anthropologists such as Alfred Reginald Radcliffe-Brown argued that kinship was based on descent from a common ancestor, Lévi-Strauss argued that kinship was based on the alliance between two families that formed when women from one group married men from another.” So Levi-Strauss advances. But, really, I’d eagerly go to a seminar from either of them.

For today we have a classic matchup, arguably the best pairing of the tournament so far. The director of what is said to be the worst movie ever made, vs. one of the most amazing political activists of the twentieth century. Before she made her name stopping the Equal Rights Amendment in the 1970s, Schlafly was an early supporter of Barry Goldwater and also wrote a wacky book in 1960 arguing that America needed bigger nuclear bombs. On the other hand, Ed Wood could have some excellent Hollywood stories. He directed Bela Lugosi’s last movie!

P.S. Fun fact: I first heard about Plan 9 from Outer Space in a book on the worst movies ever made, written by Michael Medved, who later gained some fame as the author of “Hollywood vs. America,” moved into a career as a conservative talk-show host and promoter of creationism. A quick google suggests that Phyllis Schlafly supports creationism too. It’s not clear if she has actual creationist beliefs or just supports the creationist movement on a general “the enemy of my enemy” principles.

P.P.S. As always, here’s the background, and here are the rules.

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]]>The post What to graph, not just how to graph it appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>While we’re on the topic of visualization, I’ve been puzzled by a more general question and I’m unsure where it fits in actually.

There seem to be two parts to a good visualization practice, and in our class we’ve been focusing more on one of them, that is “how to get my point across?” To me that’s a psychology question for which a recipe-type solution could exist, e.g. what choices of graph types, layouts and details are better at alleviating the cognitive burden in the audience, making the visual cue more salient thus a more effective graph. But this “how” question begins with the premise that I know “what point is to be made”, and what if I don’t? When doing EDA in high-dimensional data, how does one visualize the potentially multi-way joint dependency among variables? Or when checking the performance of a high-dim model, can I see anything (estimation, model fit, prediction) beyond uni-/bi-variately? And to throw in something inspired by immediate research, suppose one wishes to compare the posterior inference between MCMC and approximate inference methods (e.g. VB or EP), what can be done to display the loss of authenticity of the approximation, beyond the dry metrics such as KL divergence or marginal likelihood? I call them dry since they aren’t revealing of the specific problem, such what areas of the support or which dimensions are missing out the most. The challenge is that we’re exactly relying on visualization to teach ourselves about the behavior of this object (data or model) that would otherwise been impossible to learn, but if we are ignorant of the object, how to visualize it to begin with? To this end I think this is perhaps fundamentally a statistical methodology question, whose solution calls for a different kind of ingenuity; after all if I knew how to display a higher dimensional object in lower dimension with all essence captured, I would’ve found a good inferential method. People say that if an MCMC sampler doesn’t converge, chances are the model is problematic to begin with, and I wonder if it’s the same with statistical visualization: if I can’t display it properly, am I just asking the wrong questions?

My reply: There are a few interesting ideas here:

First, we do spend lots of time on the details of how to graph a particular idea or pattern of information, but not so much time on what to graph.

Second, there’s the challenge of trying to discover the unexpected in high dimensions. It’s my impression that there was a lot of research on this in the 1970s and 1980s: The statistics world was pretty small back then, and after Tukey started writing about exploratory data analysis, various people started working on ideas such as rotating point clouds, or automatically searching for interesting dimensions for graphical comparisons. My guess is that a lot of this work is still valuable and worth looking into further. The idea seems very powerful, to treat the human and the computer as a pattern-recognition team, and to have an algorithm to find projections that are worth further exploration.

Third, how to you compare in high dimensions, for example if you have multiple chains of HMC and you want to check that they’re pretty much in the same place? Steve Brooks and I did some work on this awhile ago with the multivariate potential scale reduction factor but it didn’t work so well in practice, perhaps because our goals weren’t so clear.

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]]>OK, yesterday‘s winner: what can you say? Leonardo da Vinci vs. The guy who did Piss Christ. The funniest argument in all the comments came from Anonymous, who wrote:

Serrano. Any schmuck can paint the Mona Lisa, but good luck finding someone who can piss in a jar.

After reading that, I really wanted to give it to the pisser, but ultimately I was swayed by Mark’s rational argument: “I suspect that Da Vinci would have appreciated Piss Christ himself, and, in fact, I think that Da Vinci might have created such a work of art himself… had they (or he), of course, invented either photography or plastic by then. So, Da Vinci could have done Piss Christ, had he only had the tools, plus all of the other cool stuff that he actually did, so . . . Da Vinci.”

And now, for today we have another contest between two modern French intellectuals. No, Claude Levi-Strauss is not the blue jeans guy. And, no, Raymond Aron is not Stanislaw Ulam’s brother-in-law.

As a political scientist, I’d rather see Aron, but it may be that Levi-Strauss would be the better speaker. Aron was great, but his big shtick was anti-Communism and that’s pretty much a dead issue today, whereas Levi-Strauss’s work continues to be relevant to social science. Either one would have a tough slog against Leonardo *or* the guy who did Piss Christ.

P.S. As always, here’s the background, and here are the rules.

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]]>Thought you might be interested in or might like to link to the following article. The statistical rigor is obviously not at a professional level, but pitched somewhere around the Bill Jamesian level.

Here’s the link. This sort of thing makes me realize how out of it I am, when it comes to thinking about baseball!

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]]>We’re happy to announce the release of Stan 2.6, including RStan, PyStan, CmdStan; it will also work with the existing Stan.jl and MatlabStan. Although there is some new functionality (hence the minor version bump), this is primarily a maintenance release. It fixes all of the known memory issues with Stan 2.5.0 and improves overall speed by around 5–10% (this will vary by model).

As usual, you can get everything you need starting from the

Please let us know if you run into any problems with installation or need help using Stan, ideally through the Stan Users Group (also linked from the home page).

These are the release notes for the underlying C++ code for Stan, which is shared by all interfaces.

v2.6.0 (5 February 2015) ====================================================================== New Features ---------------------------------------- * log_mix function for binary mixtures [half of #970] * neg_binomial_2 CDF function [#1129] Language Enhancements ---------------------------------------- * allow local-variable integers to be treated as constants, allowing, among other things, more general ODE functions [#1131] * initialize transformed data and generated quantities the same way as transformed parameters [#1099] * libstan.a removed and model compilation is 100% header-only [#1095] * check for infinite/nan inits from boundary initializations with warning [#537] API/Build/Test Enhancements ---------------------------------------- * removed extra variables being generated by validation [#1248] * generalize OperandsAndPartials to second order [#676] * rationalize error handling, using const char* args for efficiency [#1234] * upgrade to Boost version 1.55 library [#1206] * generate .hpp instead of .cpp for models [#803] * value_of_rec recursive metaprogram to pull values out of higher-order autodiff structures; removes order dependency on fwd/rev includes [#1232] * tests now run more efficiently with Python script [#1110] * add performance tests to continuous integration to perform end-to-end regression testing on speed [#708, #1245] * manual index parser for tool automation in interfaces (such as auto-complete suggestions) and emacs mode * refactor log determinant and fix return type cast in vector arena alloc [#1187] * update makefile dependencies for auto-generated cpp test files [#1108] * move test models into clearly labeled directories (good/bad) [#1016] * removing policies from math error checking [#1122] * remove usage of Boost's formatting (which can cause runtime errors like printf) [#1103] * rearrange directory structure of err_handling [#1102] * clean up math includes to proper location in source tree [#778] * remove Windows newline and write script to catch in future [#1233] * remove extra copy of Eigen (3.2.0), leaving just Eigen 3.2.2 [#1206] * remove example-models dependency from Git [#1105] Bug Fixes ---------------------------------------- * allow identifiers with prefixes matching keywords [#1177] * allow functions to be used in variable declaration constriants [#1151] * fix segfault resulting from multivariate normal in optimizer (root cause wasn't in optimizer, but in autodiff nesting cleanup) [#1200] * fixed return type in language and C++ for append_row of two column vectors and append_col of two row vectors [#1241] * fixed derivative error for pareto_type_2_log() [#1223] * remove unused models from stan distribution (they're now in the stan-dev/example-models repo on GitHub) [#1249] * squash compiler warnings and fix windows tests (mostly signed vs. unsigned, as usual) [#1257] * fix memory leak in ODE solver [#1160] * fix overflow in gamma_p function to throw instead [#674] * make sure multiply_lower_tri_self_transpose returns a symmetric matrix [#1121] * fix overflow in Poisson RNG to throw instead [#1053] Documentation ---------------------------------------- * manual updated for 2.6 [#1081] - new chapter on software process (thanks to Sebastian Weber and Tony Rossini for help) - new chapter on sparse and ragged arrays - pointers to Julia and MATLAB interfaces (yay!) - vectorization of CDFs described - fix priors on IRT models - added discussion of multiple change points - remove range-contstrained scale priors - clarified fixed parameter call - remove references to "std::vector" in favor of "array" - corrected signs for lasso and ridge and discuss truncated gradient and absolute zero results - extended discussion of Gaussian process priors (thanks to Aki Vehtari, Jon Zelner, and Herra Huu) - remove bogus paths to models and replace with pointers to example-models repo - clarified Wishart/inverse Wishart parameterizations w.r.t. BDA3 - fixed exp_mod_normal definition - fix student-t reparameterization - fix hurdle distribution definition Thanks! ---------------------------------------- Thanks to all the users who contributed code and doc corrections and comments: Alex Zvoleff, Juan Sebastián Casallas, Gökçen Eraslan, seldomworks [GitHub handle], Avraham Adler, Sebastian Weber, Amos Waterland, David Hallvig, Howard Zail, Andre Pfeuffer, Bobby Jacob, Cody Ross, Krzysztof Sakrejda, Andrew Ellis, John Sutton

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]]>The post Leonardo da Vinci (1) vs. The guy who did Piss Christ appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>But, in comments, Kyle came in with a pretty powerful argument:

I’m afraid you couldn’t get Camus to stay anywhere he couldn’t smoke.

Columbia’s no-smoking-indoors rule is, I think, inflexible.

We also have this from Jonathan: “If I had to come up with some justification, it would be that Camus would best appreciate the irony of being pitted against the least well-known person in the entire tournaent in the very first round and still losing.”

In a similar vein, Nick argued: “Like almost everybody else I have no idea who Bruno Latour is. I guess the best argument in favor of him is that the whole “must see” speaker idea came from him, so he really must be something! Of course, even if he isn’t, just think of the exclusivity of attending his seminar! You will be the envy of your friends and coworkers! And because they will never have the chance to see him themselves they will be none the wiser. He’s the ideal seminar speaker because he is ‘the ideal seminar speaker.’ Tough to out-absurd the absurdity of this situation.” Indeed.

So a Latour victory would be the best story. But then came this late entry from Person:

Camus might give an exception with his smoking rule to such an important speech (If it is on a blog it is of utmost importance) and would have some pretty good information. Latour, on the other hand… Well, do you want to go to a seminar in which you have to write an essay showing why you should go and listen the amazing Bruno Latour speek? I personally, would go with Camus.

Good point. All jokes aside, we’d much rather hear Camus, and we wouldn’t even have to write an essay to prove ourselves worthy of him. So it’s the Algerian who advances to the next round.

Sorry, Bruno. You can be consoled that in the real world you’re the one who got to speak at Columbia. Click here to see the full video, which is a stunning 1 hour and 22 minutes long. (I’ll leave it to someone with a higher boredom threshold than me to actually watch the thing.) You also got to have your play Gaia Global Circus presented “with support from The Cowles Charitable Trust and The Fan Fox and Leslie R. Samuels Foundation, with assistance from the Brown Institute for Media Innovation at the Columbia Journalism School and Alliance (Columbia, École Polytechnique, Sciences Po, and Panthéon-Sorbonne University), and in part by public funds from New York City Department of Cultural Affairs in partnership with the City Council and New York State Council on the Arts with the support of Governor Andrew Cuomo and the New York State Legislature.”

And now today’s contest:

It’s a classic matchup. The greatest artist of all time (who is also the #2 “Leonardo” on all of Google) vs. the creator of one of the most controversial artworks of 1989.

Who do you want as a seminar speaker? My first inclination is to give the nod to Leonardo, cos he could also talk about science, and maybe after a few drinks he’d let slip some clues about what’s the deal with the Da Vinci code. On the other hand, the guy who did Piss Christ probably has a lot of good stories. And, if we publicized his talk widely enough, we could probably get lots of protestors and that could really rock the house.

So it’s a tough call. What are your thoughts?

P.S. As always, here’s the background, and here are the rules.

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]]>The post When the evidence is unclear appeared first on Statistical Modeling, Causal Inference, and Social Science.

]]>Anyway, I sent the link to the Tanguy et al. paper to political scientist John Bullock, and he replied:

* I [Bullock] would be interested in reading, on your blog, or elsewhere, an appraisal of “classic” empirical political science books like Voting or The American Voter. Those books are still highly respected and widely assigned. Their analyses are simple. But when I read them, it’s sometimes tough for me to figure out exactly what the authors are doing. I wonder whether you would have a similar problem.

* You don’t mention authors’ incentives to be unclear. I fear that they are real. If you make things simple, some people will appreciate it. But some will think that you are simple-minded, and they will penalize you.

Perhaps this reasoning doesn’t explain the lack of clarity that you noted in the Tanguy et al. paper. But I do think that it helps to explain why social scientists are rarely as clear as they should be, especially at the level of the sentence and the paragraph.

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