Today's vocabulary term is "flack-to-hack ratio"

Felix Salmon has one of those that-explains-a-lot posts up on his blog:

Quartz, in this deal, is getting one article, which needs a fair amount of editing; it’s a tiny proportion of Quartz’s daily output. Meanwhile, Brandtone is getting something very valuable indeed. Just look at the US flack-to-hack ratio: it’s approaching 9:1, according to the Economist, which means that for every professional journalist, there are nine people, some of them extremely well paid, trying to persuade that journalist to publish something about a certain company. That wouldn’t be the case if those articles weren’t worth serious money to the companies in question.

How valuable? How about somewhere between $250,000 and $1 million? That’s the amount of money that Fortune’s ad-sales team was asking, earlier this month, for a new product called Fortune Trusted Original Content:

Similar to licensed editorial content, TOC involves creating original, Fortune-branded editorial content (articles, video, newsletters) exclusively for marketers to distribute on their own platforms.

After news of the TOC program appeared, it was walked back — abolished, essentially. You can see why Fortune’s top editorial brass would be uncomfortable with the idea that Fortune editorial content could be commissioned by, and appear for the sole benefit of, advertisers. So now they’re going back to the old model, of just allowing advertisers to license (reprint, basically) stories which were independently commissioned and published by Fortune’s editors.

Still, the price point on the now-aborted TOC program is revealing. The cost of the content, from a “trusted freelancer”, would probably not be much more than a couple of thousand dollars — but the cost of the content to the advertiser could be as much as $1 million. The difference is entirely accounted for by the value of the Fortune brand.

The flack-to-hack ratio may have something to do with another recurring topic, the almost complete lack of coverage of the reemergence of over-the-air television (see here, here, here, here, and... hell, just do a search). Weigel Broadcasting may be an extraordinarily well run company, but as long as they run a largely flackless operation, you'll probably never hear about them.

I apologize if I posted this before...

... but this Marketplace piece on a program to get disadvantaged families out of bad neighborhoods is definitely worth checking out.

Weekend movie blogging -- Herman Mankiewicz in Oz

With Oz, the Great and Powerful being both at the box office, it's worth taking a minute to give a little credit to someone who made an essential but largely unrecognized contribution to the original classic, Herman Mankiewicz. Famed film historian/director/sycophant, Peter Bogdanovich has spent the past few decades trying to undermine Mankiewiez's reputation since Pauline Kael had the temerity to suggest that Mankiewicz was the primary author of the script of Citizen Kane.

Bogdanovich has sold the "Herman Mankiewicz was a talented hack" line to countless credulous journalists and film students over the years and supported the claim with a highly selective recounting of Mankiewiez's resume.  With Oz back in such a big way, one of the films Bogdanovich omits is particularly relevant:

In February, 1938, he was assigned as the first of ten screenwriters to work on The Wizard of Oz. Three days after he started writing he handed in a seventeen-page treatment of what was later known as "the Kansas sequence". While Baum devoted less than a thousand words in his book to Kansas, Mankiewicz almost balanced the attention on Kansas to the section about Oz. He felt it was necessary to have the audience relate to Dorothy in a real world before transporting her to a magic one. By the end of the week he had finished writing fifty-six pages of the script and included instructions to film the scenes in Kansas in black and white. His goal, according to film historian Aljean Harmetz, was to "to capture in pictures what Baum had captured in words--the grey lifelessness of Kansas contrasted with the visual richness of Oz." He was not credited for his work on the film, however.

There are, of course, many things that have to go right to produce a truly iconic film, but if you had to pick the one element that made the film work and made people remember it, you'd probably have to go with Mankiewicz's contribution.

Playing with paper over at You Do the Math

Been thinking a lot about paper from a material-science standpoint (the mind wanders when SAS runs slowly), specifically about using paper to teach kids about the physical properties of different shapes and how we test them.

I've kicked off an ongoing thread on the subject at my math teaching blog. The first (with the admittedly pretentious title, "Reseeing Paper") is an overview of paper as a way of exploring shape. The second ("Paper Towers") lays out the rules for some engineering projects/competitions suitable (almost without alteration) for classes ranging from fourth grade to freshman engineering (though one would like to think that the results for the freshmen would be a bit more sophisticated). The projects would also be suitable for science-based family activities. There is more of this to come (I haven't even started on corrugation).

Check it out and if you know of any teachers who are looking for new (and cheap) manipulatives, please send them the links. There are any number of potential lesson plans here.

Thanks,
Mark

p.s. Suggestions are always welcome.

When you hear proposals to control tuition by reducing instruction cost...

When you hear proposals to control tuition by reducing instruction cost (in the recent discussions of MOOCs for example), here are a couple of numbers you should keep in mind. They haven't been adjusted for inflation and they're based on a sample size of one, but I still thing they'll gave you a pretty clear picture.

Back in the Nineties I did a four year stint as a lecturer at a large state university. The standard load for lecturers was four courses a semester and the pay was seventeen thousand and change. (I was only on a half load with the other half of my funding coming from other duties like supervising grad students but the following numbers still hold).

If you break it down that comes to less than twenty-five hundred a three hour course. With the exception of a couple of upper level courses, my sections generally ranged from twenty-five to one hundred and fifty students. That means that the per student cost associated with the lecture portion of one of those courses ranged from less than one hundred dollars at the top end to around fifteen dollars at the bottom.

If someone has some current numbers I'd be glad to update the post but as far as I can tell, while tuition has continued to climb since my lecturer days, adjunct salaries have, at best, kept up with inflation and certainly haven't grown enough to be a major driver of education costs. But what's really amazing isn't that you can get people to take these jobs at this pay; it's that you can find wildly overqualified people -- promising scholars, gifted lecturers -- willing to take these jobs. That's how flooded the supply of would-be professors is.

There are well-paid, even over-compensated professors out there but they are all paid primarily for something other than teaching, be it their research or their reputation (which reflects on the school) or the grants they pull in. We can and probably should have a serious discussion about these roles (maybe starting here) but that's a different conversation.

As for controlling tuition by reducing instructor costs, that conversation has to start with a realistic picture of how much people who are hired simply to teach actually make.

The Rise of P-Value

In the middle of a recent conversation prompted by this post by Andrew Gelman, I struck me that I couldn't recall encountering the term p-value before I started studying statistics in the Nineties. These days you frequently run across the term in places like the NYT article Gelman cited or this piece in the Motley Fool; were they always there and I just missed them?

Fortunately we have Google's Ngram viewer to resolve these questions and apparently the answer is a qualified yes. While people were talking about p-values at the beginning of the decade, more people were talking about them by the end.

The question now is how much of that growth is attributable to general interest writing like the NYT.

Epidemiology and Truth

This post by Thomas Lumley of Stats Chat is well worth reading and thinking carefully about.  In particular, when talking about a study of process meats and mortality he opines:

So, the claims in the results section are about observed differences in a particular data set, and presumably are true. The claim in the conclusion is that this ‘supports’ ‘an association’. If you interpret the conclusion as claiming there is definitive evidence of an effect of processed meat, you’re looking at the sort of claim that is claimed to be 90% wrong. Epidemiologists don’t interpret their literature this way, and since they are the audience they write for, their interpretation of what they mean should at least be considered seriously.

I think that support of an association has to be the most misunderstood piece of Epidemiology (and we epidemiologists are not innocent of this mistake ourselves).  The real issue is that cause is a very tricky animal.  It can be the case that complex disease states have a multitude of "causes".

Consider a very simple (and utterly  artificial) example.  Let assume (no real science went into this example) that hypertension (high systolic blood pressure) occurs when multiple exposures over-whelms a person's ability to compensate for the insult.  So if you have only one exposure off of the list then you are totally fine.  If you have 2 or more then you see elevated blood pressure.  Let's make the list simple: excessive salt intake, sedentary behavior, a high stress work environment, cigarette smoking, and obesity.  Now some of these factors may be correlated, which is its own special problem.

But imagine how hard this would be to disentangle, using either epidemiological methods or personal experimentation.  Imagine two people who work in a high stress job, one of which eats a lot of salt.  They both start a fitness program due to borderline hypertension.  One person sees the disease state vanish whereas the other sees little to no change.  How do you know what was the important factor?

It's easy to look at differences in the exercise program; if you torture the data enough it will confess.  At a population level, you would expect completely different results depending on how many of these factors the underlying population had.  You'd expect, in the long run, to come to some sort of conclusion but it is unlikely that you'd ever stumble across this underlying model using associational techniques. 

The argument continues:

So, how good is the evidence that 90% of epidemiology results interpreted this way are false? It depends. The argument is that most hypotheses about effects are wrong, and that the standard for associations used in epidemiology is not a terribly strong filter, so that most hypotheses that survive the filter are still wrong. That’s reasonably as far as it goes. It does depend on taking studies in isolation. In this example there are both previous epidemiological studies and biochemical evidence to suggest that fat, salt, smoke, and nitrates from meat curing might all be harmful. In other papers the background evidence can vary from strongly in favor to strongly against, and this needs to be taken into account.
 

This points out (correctly) the troubles in just determining an association between A and B.  It's ignoring all of the terrible possibilities -- like A is a marker for something else and not the cause at all.  Even a randomized trial will only tell you that A reduces B as an average causal effect in the source population under study.  It will not tell you why A reduced B.   We can make educated guesses, but we can also be quite wrong.

Finally, there is the whole question of estimation.  If we mean falsehood to be that the size of the average causal effect of intervention A on outcome B is completely unbiased then I submit that 90% is a very conservative estimate (given if you make truth an interval around the point estimate to the precision of the reported estimate given the oddly high number of decimal places people like to quote for fuzzy estimates). 

But that last point kind of falls into the "true but trivial" category . . .

Landscapes in everything

SLIGHTLY UPDATED

One of the issues I have with economics exceptionalism is the word 'everything,' as in "markets in everything" or "the hidden side of everything." Not that there's anything wrong with applying economic concepts to a wide variety of questions (I do it myself), but at some point they become overused and start crowding out ideas that are better in a given context.

Think about all the times you heard phrases like the 'marriage market' often followed by the implicit or explicit suggestion that the tools of economics hold the key to understanding all sorts of human behavior even in cases where the underlying assumptions of those tools probably don't apply. Now, for example, compare that to the number of times you've recently heard someone describe something as a fitness landscape when they weren't talking about evolution or physics (OK, that's not the term physicists generally use but the concept is basically the same).

Landscapes are a powerful and widely applicable concept, arguably more so than markets (they are also a long-time fascination of mine). Ideas like gradient searches, perturbation, annealing and, most of all, local optimization are tremendously useful, both to explain complex problems and to suggest approaches for solving them. Once you start thinking in those terms you can see landscapes about as often as Tyler Cowen sees markets.

You can even find researchers coming up with the kind of unexpected, everyday examples that you might expect in a Steven Levitt column.

My favorite recent example (at least recent to me) is T. Grandon Gill's observation that recipes in a cookbook are essentially the coordinates of local optima on a culinary fitness landscape where the amount of each ingredient are the dimensions and taste is the fitness function (technically we should add some dimensions for preparation and make some allowance for the subjectivity of taste, but I'm keeping things simple).

This is a great example of a rugged landscape that everyone can relate to. You can find any number of delicious recipes made with the same half dozen or so ingredients. As you start deviating from one recipe (moving away from a local optima), the results tend to get worse initially, even if you're moving toward a better recipe.

Approaching something as a rugged landscape can provide powerful insights and very useful tools, which leads to another concern about economic exceptionalism -- economics as a field tends to make little use of these models and many economists routinely make modeling assumptions that simply make no sense if the surface being modeled really is rugged.

I asked Noah Smith* about this and as part of his reply he explained:

But for analyzing the equilibrium state of the economy - prices and quantities - economists tend to try as hard as they can to exclude multiple equilibria. Often this involves inventing arbitrary equilibrium criteria with zero theoretical justification. This is done routinely in micro (game theory) as well as in macro. An alternative procedure, commonly used in macro by DSGE practitioners, is to linearize all their equations, thus assuring "uniqueness". Some researchers are averse to this practice, and they go ahead and publish models that have multiple equilibria; however, there is a strong publication bias against models that have multiple equilibria, so many economists are afraid to do this. An exception is that some models with two equilibria (a "good" equilibrium and a "bad" or "trap" equilibrium) do get published and respected. Models with a bunch of equlibria, or where the economy is unstable and tends to shift between equilibria on its own at a high frequency, are pretty frowned upon.

This doesn't mean that economists can't work with these concepts, but it does mean that as economists increasingly dominate the social sciences, approaches that don't fit with the culture and preferred techniques of economics are likely to be underused.

And some of those techniques are damned useful.

* now with source.

Some epidemiology for a change

John Cook has an interesting point:

When you reject a data point as an outlier, you’re saying that the point is unlikely to occur again, despite the fact that you’ve already seen it. This puts you in the curious position of believing that some values you have not seen are more likely than one of the values you have in fact seen.
 

This is especially problematic in the case of rare but important outcomes and it can be very hard to decide what to do in these cases.  Imagine a randomized controlled trial for the effectiveness of a new medication for a rare disease (maybe something memory improvement in older adults).  One of the treated participants experiences sudden cardiac death whereas nobody in the placebo group does. 

One one hand, if the sudden cardiac death had occured in the placebo group, we would be extremely reluctant to advance this as evidence that the medication in question prevents death.  On the other hand, rare but serious drug adverse events both exist and can do a great deal of damage.  The true but trivial answer is "get more data points".  Obviously, if this is a feasible option it should be pursued. 

But these questions get really tricky when there is simply a dearth of data.  Under these circumstances, I do not think that any statistical approach (frequentist, Bayesian or other) is going to give consistently useful answers, as we don't know if the outlier is a mistake (a recording error, for example) or if it is the most important feature of the data.

It's not a fun problem. 

More weekend work avoidance -- the pleasures of microbudgets

Watched the first and second arcs of a fairly obscure British science fiction show from 1979 called Sapphire and Steel. It was apparently intended as a low-budget answer to Doctor Who (which those familiar can attest was not exactly the Avatar of the Seventies).  The result was a sci-fi/fantasy/horror show that had to be shot on standing sets with small casts and very limited special effects.

The result is some really impressive constrained problem solving by writer P.J. Hammond (with considerable assistance from directors David Foster, Shaun O'Riordan and the show's solid leads, David McCallum and Joanna Lumley, the only expensive aspects of the production). Hammond did sometimes lapse into dramatic Calvinball, obviously making up new rules now and then to get himself out of narrative corners, but those bits are easy to overlook, particularly when watching the ways he found to work around the rules he was handed by the producers.

In lieu of optical effects and creature make-up, you get a spot of light on the floor, a shadow on the wall, an ordinary thing in a place it shouldn't be. In an ironic way, the show would almost certainly look cheaper now if they had spent the extra money on those late Seventies effects then. In a sense, they didn't have enough money to be cheesy (except perhaps in the opening title).

There's a bigger point to be made about the costly vs. the clever but the weekend is almost up and my work is going to be unavoidable in a few hours.

Weekend gaming -- new entries at You Do the Math

I've got three big ongoing threads planned for my teacher support blog, one on the SAT and one on a special class of manipulatives, and one on teaching programming, so naturally I've been avoiding those topics and writing about games instead: If you also have an interest in games and work to avoid, you might drop by and check out:

The Exact Chaos Game -- fleshing out a suggestion by John D. Cook, this lets players bet on iterations of a surprisingly unpredictable function.

Kriegspiel and Dark Chess -- more Wikipedia than me but worth checking out if you'd like to see what chess might look like as a game of imperfect information.

Facade Chess -- along the same lines, here's an "original" imperfect-information variant where a subset of the  pieces may be disguised as other pieces.

Do op-ed writers provide their own hyperlinks?

Or is some intern handed the copy and told to find some appropriate citations? I generally assume that the links are an intrinsic part of anything written specifically for online consumption but what about the online version of something primarily intended for print?

Take this op-ed by Joe Scarborough and Jeffrey D. Sachs writing for the Washington Post which starts with the following paragraph:

Dick Cheney and Paul Krugman have declared from opposite sides of the ideological divide that deficits don’t matter, but they simply have it wrong. Reasonable liberals and conservatives can disagree on what role the federal government should play yet still believe that government should resume paying its way.

As a commenter on Krugman's blog pointed out, if you click on Krugman's name in that paragraph, you'll end up at a post that starts as follows:

Right now, deficits don’t matter — a point borne out by all the evidence. But there’s a school of thought — the modern monetary theory people — who say that deficits never matter, as long as you have your own currency.

I wish I could agree with that view — and it’s not a fight I especially want, since the clear and present policy danger is from the deficit peacocks of the right. But for the record, it’s just not right.

In other words, to support the claim that Krugman said deficits don't matter, Scarborough and Sachs point to Krugman saying explicitly that people who say deficits don't matter are wrong. Krugman then spends pretty much the entire post arguing that deficits will matter a great deal once we're out of the liquidity trap. Here's the key section.

So we’re talking about a monetary base that rises 12 percent a month, or about 400 percent a year.

Does this mean 400 percent inflation? No, it means more — because people would find ways to avoid holding green pieces of paper, raising prices still further.

I could go on, but you get the point: once we’re no longer in a liquidity trap, running large deficits without access to bond markets is a recipe for very high inflation, perhaps even hyperinflation. And no amount of talk about actual financial flows, about who buys what from whom, can make that point disappear: if you’re going to finance deficits by creating monetary base, someone has to be persuaded to hold the additional base.

This isn't to say that this post is in agreement with the op-ed; in terms of immediate action they are taking completely opposite positions, It would have easy to spell out the distinction, but instead Scarborough and Sachs simply make a claim then point us to something that directly contradicts it.

The strange thing here is that you could find any number of posts where Krugman focuses on the case for stimulus and largely or entirely ignores the dangers of deficits. Any of these would have supported Scarborough and Sachs' thesis. Instead, though, the authors pick possibly the strongest anti-deficit argument Krugman has made in the past five years.

I can understand Scarborough. He is, and I don't mean this as a pejorative, a TV personality. That's a rare and valuable talent and Scarborough is very good at it. It is not, however, a profession that depends upon reputation in the conventional sense. As long as a TV personality does nothing to betray his public persona, almost all press is good press.

For Sachs, though, reputation is extraordinarily important. This is an important and influential scholar, someone whose ideas carry great weight with policy makers. Here's a representative passage from Wikipedia:

Sachs is the Quetelet Professor of Sustainable Development at Columbia's School of International and Public Affairs and a Professor of Health Policy and Management at Columbia's School of Public Health. He is Special Adviser to United Nations Secretary-General Ban Ki-Moon on the Millennium Development Goals, having held the same position under former UN Secretary-General Kofi Annan. He is co-founder and Chief Strategist of Millennium Promise Alliance, a nonprofit organization dedicated to ending extreme poverty and hunger. From 2002 to 2006, he was Director of the United Nations Millennium Project's work on the Millennium Development Goals, eight internationally sanctioned objectives to reduce extreme poverty, hunger, and disease by the year 2015. Since 2010 he has also served as a Commissioner for the Broadband Commission for Digital Development, which leverages broadband technologies as a key enabler for social and economic development.

Silly, avoidable errors undercut Sachs' ability to continue this good work.

Which leads back to my original question. Did Jeffrey Sachs actually agree upon a link that contradicted the point he was trying to make or are links, like headlines and blurbs, often added after a piece is submitted?

More on Marissa Mayer

I think that this is a very good point:

It also seems like a feminist mistake to expect women entrepreneurs to create little utopias instead of running extremely successful businesses. Mayer was attacked recently for her decision not to allow employees to work at home. She is a woman, this line of thinking goes, how could she think women should have to work away outside of their houses, away from their children? But why should Marissa Mayer have some special responsibility to nurture her employees with a cozy, consummately flexible work environment just because she is a woman? Isn’t her responsibility to run a company according to her individual vision? If we want powerful female entrepreneurs shouldn’t we allow them to pursue entrepreneurial power?
 

 I am not actually 100% sure that the decision to end "work at home" really hurt woman at Yahoo! (as a class, clearly individual workers of both genders could have had their work lives disrupted) given that men are more likely to work at home than women.  Mayer's previous company (Google) tries to limit the number of telecommuters and it is hardly unreasonable that a new CEO would want to draw on successful business models that she has personal experience with. 

Now could this policy change have been done more artfully? Sure.  But I am amazed by the duration of this discussion in the media and how much insight it is bringing into the whole work at home phenomenon. 

Admittedly, it is a competitive field

Thomas Lumley is an early contender for identifying the worst chart of 2013.  This special breed of awful is accomplished by creating a chart that actually takes more effort to process than text describing the differences would.  Since the point of charts is to convey information efficiently, there really is no good reason for this chart to exist. 

Of course, as a long time SAS programmer I am biased against graphical displays of data in general (you would be too if you had to use gplot and gchart).  But I think that this example will be disliked by the R and STATA crowd too. 

Forwarded almost without comment

This story from Reuters is outside of my area of expertise so I'm just going to make this blanket recommendation. This is a solid piece of reporting on the not easy-to-cover fields of epidemiology, biostatistics and the economics of health care.

Special Report: Behind a cancer-treatment firm's rosy survival claims

Edit (Joseph): Andrew Gelman correctly points out that the authors are Sharon Begley and Robin Respaut.  This report is useful to me as another reason that we need to have a control arm for randomized trials.  It isn't enough to know what the rate is for conventional care and contrast a novel therapy with it.  You need to also account for the selection effects among the population receiving the novel therapy.  Randomization is a very nice way to accomplish this outcome in a generally understood manner.