Tom Wolfe

I’m a big Tom Wolfe fan.

My favorites are The Painted Word and From Bauhaus to Our House, and I have no patience for the boosters (oh, sorry, “experts”) of modern art of the all-black-painting variety or modern architecture of the can’t-find-the-front-door variety who can’t handle Wolfe’s criticism.

I also enjoyed Bonfire of the Vanities, with my only real complaint being the ending. Or maybe I should say the structure of the book. Wolfe sets all these great characters and plot elements in motion and then he doesn’t really resolve anything; it was kinda like he just got tired and decided to stop. That said, endings are hard. Robert Heinlein and Roald Dahl are two other writers who were great in the set-up but often had problems with the follow-through.

The characters in Bonfire were two-dimensional, but I can attribute that to Wolfe being more of a reporter than a novelist. When you’re reporting, you don’t need to flesh out your characters’ full dimensionality because, after all, they’re real people—as a reporter, you’re just telling part of the story. With a novel you have to put in that extra effort. In any case, two-dimensional ain’t bad. Gone Girl was pretty good, and all its characters were one-dimensional. 1984 is a great novel, and one might argue that its characters are zero-dimensional. The Rotter’s Club, these guys are three dimensional, but he’s Jonathan Coe, for chrissake. And Rabbit’s positively four-dimensional (remember, time is the fourth dimension), but Rabbit’s the greatest creation of a great novelist. Bonfire of the Vanities is an excellent book that we should define by its many strengths—its vividness, its up-to-the-minuteness, its Dickensianness, etc.—not by its few weaknesses.

What else? I never read The Right Stuff—the movie was so good, I felt no need to read the book—and was never able to get through his classic reportages of the car guys and the surfers and all those other pieces from the 60s. I found the whole Style!!! thing just too exhausting. It’s not that I’ll never read stream of consciousness—I read On the Road back when I was 20 or so, and it was great!—but something about those Wolfe essays, they just seemed so willed. I’m sure they were great at the time, but from the standpoint of decades later, I find the understated style of Gay Talese much more convincing. I did, however, like some of Wolfe’s later essays, such as his justification for Bonfire (that article about the billion-footed beast) and his attack on Mailer/Irving/Updike. Perhaps it’s just my taste that I preferred Wolfe when he was writing straight.

And then there was Wolfe’s attack on evolution. That was just foolish. But, hey, nobody’s perfect. Wolfe was proud of his ability to defend ridiculous positions, and in other settings that made for great writing.

After Wolfe died, I read a bunch of obituaries. And I learned a few things.

First off, I learned that he was tall. Who knew? In all those photos, I just somehow assumed he was short. Really short, like 5’3″ or something. Maybe it was how he dressed, like a dandy?

I also learned that Wolfe was middle-of-the-road, politically. I’d always thought of him as conservative, but I guess that was just in comparison to the rest of the literary establishment. According to Kyle Smith, he “habitually voted for the winner in every presidential election, except when he picked Mitt Romney in 2012 and Ross Perot in 1992.”

Finally, I learned that, in his famous article, Radical Chic, Wolfe was unfair to Leonard Bernstein. By this I don’t mean that he was making fun of Bernstein, quoting Bernstein out of context, not showing sufficient respect for Bernstein, etc. What I mean is that he put words into Bernstein’s mouth, put thoughts in Bernstein’s head, based on no evidence at all. Jay Livingston has the story. I’d never actually read that particular essay so I had no idea, and it didn’t come up in any of the other obituaries that I read. I guess maybe Radical Chic should be taken as fiction or satire; Bernstein was a public figure; you can say what you want about public figures if you’re writing fiction or satire; in any case Wolfe was still a brilliant writer and cultural critic. Still, it made me sad to learn this. Making fun of Bernstein, fine. Attributing thoughts to him—and not just any thoughts, but thoughts that make him look particularly foolish—not so cool. Then again, Wolfe was many things but I doubt he ever would’ve claimed to be cool.

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Graphs and tables, tables and graphs

Jesse Wolfhagen writes:

I was surprised to see a reference to you in a Quartz opinion piece entitled “Stop making charts when a table is better”. While the piece itself makes that case that there are many kinds of charts that are simply restatements of tabular data, I was surprised that you came up as an advocate of tables being a “more honest” way to present information. It seems hard to see downstream effects by looking solely at tables.
So then I looked at the link, which led me to your blog post from 2009. Specifically, from April 1, 2009. Yes, like any good satire, your post was taken at face value!
So while yes, there are reasons for tables and reasons for charts and misuses of both formats, I might humbly suggest you put a tag on your future April 1 posts (on, say April 2), because it’s the internet age: satire and close inspection of dates are dead, but text searching and confirmation bias are alive and well.

Yup.  For anyone who has further interest in the particular topic of tables and graphs, I recommend this paper from 2011, which begins:

The statistical community is divided when it comes to graphical methods and models. Graphics researchers tend to disparage models and to focus on direct representations of data, mediated perhaps by research on perceptions but certainly not by probability distributions. From the other side, modelers tend to think of graphics as a cute toy for exploring raw data but not much help when it comes to the serious business of modeling. In order to better understand the benefits and limitations of graphs in statistical analysis, this article presents a series of criticisms of graphical methods in the voice of a hypothetical old-school analytical statistician or social scientist. We hope to elicit elaborations and extensions of these and other arguments on the limitations of graphics, along with responses from graphical researchers who might have different perceptions of these issues.

Still relevant seven years later, I think.

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“Using numbers to replace judgment”

Julian Marewski and Lutz Bornmann write:

In science and beyond, numbers are omnipresent when it comes to justifying different kinds of judgments. Which scientific author, hiring committee-member, or advisory board panelist has not been confronted with page-long “publication manuals”, “assessment reports”, “evaluation guidelines”, calling for p-values, citation rates, h-indices, or other statistics in order to motivate judgments about the “quality” of findings, applicants, or institutions? Yet, many of those relying on and calling for statistics do not even seem to understand what information those numbers can actually convey, and what not. Focusing on the uninformed usage of bibliometrics as worrysome outgrowth of the increasing quantification of science and society, we place the abuse of numbers into larger historical contexts and trends. These are characterized by a technology-driven bureaucratization of science, obsessions with control and accountability, and mistrust in human intuitive judgment. The ongoing digital revolution increases those trends. We call for bringing sanity back into scientific judgment exercises.

I agree. Vaguely along the same lines is our recent paper on the fallacy of decontextualized measurement.

This happens a lot, that the things that people do specifically to make their work feel more scientific, actually pull them away from scientific inquiry.

Another way to put it is that subjective judgment is unavoidable. When Blake McShane and the rest of us were writing our paper on abandoning statistical significance, once potential criticism we had to address was: What’s the alternative? If researchers, journal editors, policymakers, etc., don’t have “statistical significance” to make their decisions, what can they do? Our response was that decision makers already are using their qualitative judgment to make decisions. PNAS, for example, doesn’t publish every submission that is sent to them with “p less than .05”; no, they still reject most of them, on other grounds (perhaps because their claims aren’t dramatic enough). Journals may use statistical significance as a screener, but they still have to make hard decisions based on qualitative judgment. We, and Marewski and Bornmann, are saying that such judgment is necessary, and it can be counterproductive to add a pseudo-objective overlay on top of that.

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2018: How did people actually vote? (The real story, not the exit polls.)

Following up on the post that we linked to last week, here’s Yair’s analysis, using Mister P, of how everyone voted.

Like Yair, I think these results are much better than what you’ll see from exit polls, partly because the analysis is more sophisticated (MRP gives you state-by-state estimates in each demographic group), partly because he’s using more data (tons of pre-election polls), and partly because I think his analysis does a better job of correcting for bias (systematic differences between the sample and population).

As Yair puts it:

We spent the last week combining all of the information available — pre-election projections, early voting, county and congressional district election results, precinct results where we have it available , and polling data — to come up with our estimates.

In future election years, maybe Yair’s results, or others constructed using similar methods, will become the standard, and we’ll be able to forget exit polls, or relegate them to a more minor part of our discourse.

Anyway, here’s what Yair found:

The breakdown by age. Wow:

Changes since the previous midterm election, who voted and how they voted:

Ethnicity and education:

Urban/suburban/rural:

>

Yair’s got more at the link.

And here’s our summary of what happened in 2018, that we posted a few days after the election.

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Hey, check this out: Columbia’s Data Science Institute is hiring research scientists and postdocs!

Here’s the official announcement:

The Institute’s Postdoctoral and Research Scientists will help anchor Columbia’s presence as a leader in data-science research and applications and serve as resident experts in fostering collaborations with the world-class faculty across all schools at Columbia University. They will also help guide, plan and execute data-science research, applications and technological innovations that address societal challenges and related University-wide initiatives.

Postdoc Fellows

Requirements: PhD degree

APPLY NOW

Research Scientist (Open Rank)

Requirements: PhD degree + (see position description for more information)

Research Scientist who will conduct independent cutting-edge research in the foundations or application of data science or related fields, or be involved in interdisciplinary research through a collaboration between the Data Science Institute and the various schools at the University.

APPLY NOW

Research Scientist (Open Rank)

Requirements: PhD degree + (see position description for more information)

Research Scientist who will serve as Columbia’s resident experts to foster collaborations with faculty across all the schools at Columbia University.

APPLY NOW

Candidates for all Research Scientists positions must apply using the links above that direct to the Columbia HR portal for each position, whereas the Postdoc Fellows submit materials via: DSI-Fellows@columbia.edu

I’m part of the Data Science Institute so if you want to work with me or others here at Columbia, you should apply.

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Robustness checks are a joke

Someone pointed to this post from a couple years ago by Uri Simonsohn, who correctly wrote:

Robustness checks involve reporting alternative specifications that test the same hypothesis. Because the problem is with the hypothesis, the problem is not addressed with robustness checks.

Simonsohn followed up with an amusing story:

To demonstrate the problem I [Simonsohn] conducted exploratory analyses on the 2010 wave of the General Social Survey (GSS) until discovering an interesting correlation. If I were writing a paper about it, this is how I may motivate it:

Based on the behavioral priming literature in psychology, which shows that activating one mental construct increases the tendency of people to engage in mentally related behaviors, one may conjecture that activating “oddness,” may lead people to act in less traditional ways, e.g., seeking information from non-traditional sources. I used data from the GSS and examined if respondents who were randomly assigned an odd respondent ID (1,3,5…) were more likely to report reading horoscopes.

The first column in the table below shows this implausible hypothesis was supported by the data, p<.01 (STATA code)

People are about 11 percentage points more likely to read the horoscope when they are randomly assigned an odd number by the GSS. Moreover, this estimate barely changes across alternative specifications that include more and more covariates, despite the notable increase in R2.

Simonsohn titled his post, “P-hacked Hypotheses Are Deceivingly Robust,” but really the point here has nothing to do with p-hacking (or, more generally, forking paths).

Part of the problem is that robustness checks are typically done for purpose of confirming one’s existing beliefs, and that’s typically a bad game to be playing. More generally, the statistical properties of these methods are not well understood. Researchers typically have a deterministic attitude, identifying statistical significance with truth (as for example here).

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Chocolate milk! Another stunning discovery from an experiment on 24 people!

Mike Hull writes:

I was reading over this JAMA Brief Report and could not figure out what they were doing with the composite score. Here are the cliff notes:

Study tested milk vs dark chocolate consumption on three eyesight performance parameters:

(1) High-contrast visual acuity
(2) Small-letter contrast sensitivity
(3) Large-letter contrast sensitivity

Only small-letter contrast sensitivity was significant, but then the authors do this:

Visual acuity was expressed as log of the minimum angle of resolution (logMAR) and contrast sensitivity as the log of the inverse of the minimum detectable contrast (logCS). We scored logMAR and logCS as the number of letters read correctly (0.02 logMAR per visual acuity letter and 0.05 logCS per letter).

Because all 3 measures of spatial vision showed improvement after consumption of dark chocolate, we sought to combine these data in a unique and meaningful way that encompassed different contrasts and letter sizes (spatial frequencies). To quantify overall improvement in spatial vision, we computed the sum of logMAR (corrected for sign) and logCS values from each participant to achieve a composite score that spans letter size and contrast. Composite score results were analyzed using Bland-Altman analysis, with P < .05 indicating significance.

There are more details in the short Results section, but the conclusion was that “Twenty-four participants (80%) showed some improvement with dark chocolate vs milk chocolate (Wilcoxon signed-rank test, P < .001)." Any idea what's going on here? Trial pre-registration here.

I replied that I don’t have much to say on this one. They seemed to have scoured through their data so I’m not surprised they found low p-values. Too bad to see this sort of thing appearing in Jama. I guess chocolate’s such a fun topic, there’s always room for another claim to be made for it.

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“Law professor Alan Dershowitz’s new book claims that political differences have lately been criminalized in the United States. He has it wrong. Instead, the orderly enforcement of the law has, ludicrously, been framed as political.”

This op-ed by Virginia Heffernan is about g=politics, but it reminded me of the politics of science.

Heffernan starts with the background:

This last year has been a crash course in startlingly brutal abuses of power. For decades, it seems, a caste of self-styled overmen has felt liberated to commit misdeeds with impunity: ethical, sexual, financial and otherwise.

There’s hardly room to name them all here, though of course icons of power-madness such as Donald Trump and Harvey Weinstein are household names. In plain sight, even more or less regular schmos — including EPA administrator Scott Pruitt, disgraced carnival barker Bill O’Reilly and former New York Atty. Gen. Eric Schneiderman — seem to have fancied themselves exempt from the laws that bind the rest of us.

These guys are not exactly men of Nobel-level accomplishment or royal blood. Like the rest of us, they live in a democracy, under rule of law. Still, they like to preen. . . .

On Friday, a legal document surfaced that suggested Donald Trump and Michael Cohen, his erstwhile personal lawyer, might have known about Schneiderman’s propensity for sexual violence as early as 2013, when Trump tweeted menacingly about Schneiderman’s being a crook . . . Cohen’s office also saw big sums from blue-chip companies not known for “Sopranos”-style nonsense, specifically, Novartis and AT&T. . . .

Also this week, the Observer alleged that Trump confederates hired the same gang of former Israeli intelligence officers to frame and intimidate proponents of the Iran deal that Harvey Weinstein once viciously sicced on his victims.

Then, after this overview of the offenders, she discusses their enablers:

Law professor Alan Dershowitz’s new book claims that political differences have lately been criminalized in the United States. He has it wrong. Instead, the orderly enforcement of the law has, ludicrously, been framed as political.

As with politics, so with science: Most people, I think, are bothered by these offenses, and are even more bothered by idea that they have been common practice. And some of us are so bothered that we make a fuss about it. But there are others—Alan Dershowitz types—who are more bothered by those who make the offense public, who have loyalty not to government but to the political establishment. Or, in the science context, have loyalty not to science but to the scientific establishment.

In politics, we say that the consent of the governed is essential to good governance, thus there is an argument that, at least in the short term, it’s better to hush up crimes rather than to let them be known. Similarly, in science, there are those who prefer happy talk and denial, perhaps because they feel that the institution of science is under threat. As James Heathers puts it, these people hype bad science and attack its critics because criticism is bad for business.

Who knows? Maybe Dershowitz and the defenders of junk science are right! Maybe a corrupt establishment is better than the uncertainty of the new.

They might be right, but I wish they’d be honest about what they’re doing.

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Hey! Here’s what to do when you have two or more surveys on the same population!

This problem comes up a lot: We have multiple surveys of the same population and we want a single inference. The usual approach, applied carefully by news organizations such as Real Clear Politics and Five Thirty Eight, and applied sloppily by various attention-seeking pundits every two or four years, is “poll aggregation”: you take the estimate from each poll separately, if necessary correct these estimates for bias, then combine them with some sort of weighted average.

But this procedure is inefficient and can lead to overconfidence (see discussion here, or just remember the 2016 election).

A better approach is to pool all the data from all the surveys together. A survey response is a survey response! Then when you fit your model, include indicators for the individual surveys (varying intercepts, maybe varying slopes too), and include that uncertainty in your inferences. Best of both worlds: you get the efficiency from counting each survey response equally, and you get an appropriate accounting of uncertainty from the multiple surveys.

OK, you can’t always do this: To do it, you need all the raw data from the surveys. But it’s what you should be doing, and if you can’t, you should recognize what you’re missing.

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2018: Who actually voted? (The real story, not the exit polls.)

Continuing from our earlier discussion . . . Yair posted some results from his MRP analysis of voter turnout:

1. The 2018 electorate was younger than in 2014, though not as young as exit polls suggest.

2. The 2018 electorate was also more diverse, with African American and Latinx communities surpassing their share of votes cast in 2014.

3. Voters in 2018 were more educated than all the years in our dataset going back to 2006. . . . the exit poll shows the opposite trend. As noted earlier, they substantially changed their weighting scheme on education levels, so these groups can’t be reliably compared across years in the exit poll.

Details here.

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