Tag Archives: data mining

no free lunch in statistics

Simon and Tibshirani recently posted a short comment on the Reshef et al MIC data mining paper I blogged about a while back:

The proposal of Reshef et. al. (“MIC”) is an interesting new approach for discovering non-linear dependencies among pairs of measurements in exploratory data mining. However, it has a potentially serious drawback. The authors laud the fact that MIC has no preference for some alternatives over others, but as the authors know, there is no free lunch in Statistics: tests which strive to have high power against all alternatives can have low power in many important situations.

They then report some simulation results clearly demonstrating that MIC is (very) underpowered relative to Pearson correlation in most situations, and performs even worse relative to Székely & Rizzo’s distance correlation (which I hadn’t heard about, but will have to look into now). I mentioned low power as a potential concern in my own post, but figured it would be an issue under relatively specific circumstances (i.e., only for certain kinds of associations in relatively small samples). Simon & Tibshirani’s simulations pretty clearly demonstrate that isn’t so. Which, needless to say, rather dampens the enthusiasm for the MIC statistic.

Attention publishers: the data in your tables want to be free! Free!

The Neurosynth database is getting an upgrade over the next couple of weeks; it’s going to go from 4,393 neuroimaging studies to around 5,800. Unfortunately, updating the database is kind of a pain, because academic publishers like to change the format of their full-text HTML articles, which has a nasty habit of breaking the publisher-specific HTML parsers I’ve written. When you expect ScienceDirect to give you <table cellspacing=10>, but you get <table> with no cellspacing attribute (the horror!), bad things happen in XPath land. And then those bad things need to be repaired. And I hate repairing stuff! So I don’t do it very often. Like, once every 6 to 9 months.

In an ideal world, there would be no need to write (and fix) custom filters for different publishers, because the publishers would all simultaneously make XML representations of their articles available (in addition to HTML, PDF, etc.), and then people who have legitimate data mining reasons for regularly downloading hundreds of articles at a time wouldn’t have to cry themselves to sleep every night. But as it stands, only one major publisher of neuroimaging articles (PLoS) provides XML versions of all articles. A minority of articles from other publishers are available in XML from BioMed Central, but that’s still just a fraction of the existing literature.

Anyway, the HTML thing is annoying, but it’s possible to work around it. What’s much more problematic is that some publishers lock up the data in the tables of their articles. To make Neurosynth work, I have to be able to identify rows in tables that look like brain activations. That is, things that look roughly like this:

Most publishers are nice enough to format article tables as HTML tables; which is to say, I can look for tags like <table> and then work down the XPath tree to identify all the the rows, and then scan each rows for values that look activation-like. Then those values go into the database, and poof, next thing you know, you have meta-analytic brain activation maps from hundreds of studies. But some publishers–most notably, Frontiers–throw a wrench in the works by failing to format tables in HTML; instead, they present the tables as images (see for instance this JPEG table, pulled from this article). Which means I can’t really extract any data from them, and as a result, you’re not going to see activations from articles published in Frontiers journals in Neurosynth any time soon. So if you publish fMRI articles in Frontiers in Human Neuroscience regularly, and are wondering why I’ve been ignoring you (I like you! I promise!), now you know.

Anyway, on the remote chance that anyone reading this has any sway with people high up at Frontiers, could you please ask them to release their data? Pretty please? Lack of access to data in tables seems to be a pretty common complaint in the data mining community; I’ve talked to other people in the neuroinformatics world who’ve also expressed frustration about it, and I imagine the same is true of people in other disciplines. It’s particularly surprising given that Frontiers is, in theory, an open access publisher. I can see the data in your tables, Frontiers; why won’t you also let me read it?

Okay, I know this kind of stuff doesn’t really interest anyone; I’m just venting. The main point is, Neurosynth is going to be bigger and (very slightly) better in the near future.

in which Discover Card decides that my wife is also my daughter

Ever since I opted out of receiving preapproved credit card offers, I’ve stopped getting credit card spam in the mail (yay!). But companies I have an existing relationship with still have the right to send me various offers and updates, and there’s nothing I can do about that (except throw said offers in the trash after inspecting them and deciding that, no, I do not want to purchase the premium yacht travel insurance policy that comes with a bonus free set of matching lawn gnomes and a voucher for a buy-one-get-one-free meal at the Olive Garden). Discover Card is one of these companies, and the clever devils regularly take advantage of my amicable nature by sending me all kinds of wonderful offers. Take for instance the one I received yesterday, which starts like this:

Dear Tal,

You’ve worked for years to provide a better life for your children and prepare them for a successful future. Now that they’re in college, the overwhelming cost of higher education shouldn’t stand in the way of their success. We’re ready to help.

This is undoubtedly a very generous offer, but it comes at an inconvenient time for me, because, as it so happens, I don’t have any children right now–let alone college-aged children who need their father to front them some money. Somewhere, somehow, it seems Discover Card took a left turn at Albuquerque, when all along they were trying to get to Pismo Beach:

http://www.youtube.com/watch?v=v-s-_ME8Qns#t=1m24s

Of course, this isn’t a case of human error; I very much doubt that an overworked analyst is putting in long nights at Discover combing through random customers’ accounts looking for purchases diagnostic of college attendance (you know, like Ritalin receipts). The blame almost certainly rests with an over-inclusive algorithm that combed through my purchase history and automagically decided that I fit the profile of a middle-aged man who’s worked hard for years to provide a better life for his children. (I suppose I can take solace in the fact that while Discover probably knows what brand of toothpaste I like, it must not know my age, given that there aren’t many 31-year-old men with college-aged children.)

Anyway, I spent some time pondering what purchases I’ve made that could have tripped up Discover’s parental alarm system. And after scanning several months of statements, I’m proud to report it almost certainly has something to do with the giant monthly rent charge from “CU Residence Halls” (my wife and I live in on-campus housing). Either that or the many book-and-coffee-related charges from places with names like “University of Colorado Bookstore” and “Pretentious Coffeehouse on CU Campus”.

So that’s easy enough, right? It’s the on-campus purchases, stupid! Ah, but wait! That’s only one part of the mystery! The other, perhaps more interesting, part is this: who exactly does Discover think my college-aged child is, seeing as they clearly think I’m not the one caffeinating myself at the altar of higher education? Well, after thinking about that for a while, another clear answer emerges: it’s my wife! Discover thinks I have a college-aged daughter who also happens to be my wife! There’s no other explanation; to my knowledge, I don’t live with anyone else besides my wife (though, admittedly, I don’t check the storage closet very often).

Now, setting aside the fact that such a thing would be illegal in all fifty states, my wife and I are not very amused by this. We’re mildly amused, but we’re not very amused. But we’re refraining from making too big a fuss about it, because we’re still hoping we can get our hands on some of those sweet, sweet college loans.

In the interim, here are some questions I find myself pondering:

  • Who writes the logic that does this kind of thing? I’m not asking for names; no need to rat out your best friend who works in Discover’s data mining department. I’m just curious to know what kind of background the people who come up with these things have. Artificial intelligence? Marketing research? Dental surgery?
  • How sophisticated are the rules used to screen customers for these mailings? Is there some serious business logic operating behind the scenes that happened to go wrong here, or is a well-meaning Discover employee just running SQL queries like “SELECT name, address FROM members WHERE description LIKE ‘%residence hall%’” on their lunch break?
  • Do credit card companies that do this kind of thing (which I imagine is pretty much all of them) actually validate their logic against test datasets (in this case, a large group of Discover members whose parental status has been independently verified), or do they just pick some criteria that seem to make sense and immediately start blanketing the United States with flyers?
  • What proportion of false positives is considered reasonable? Clearly, with any kind of program like this, some small number of customers is almost invariably going to get a letter that makes some very bad lifestyle assumptions. At what point does the risk of a backlash start to outweigh the potential for increased revenue? Obviously, the vast majority of people are probably going to chalk this type of thing down to a harmless error, but I imagine some small proportion of people are going to get upset and call up Discover to rant and rave about how they don’t have any children at all, and how dare Discover mine their records like this, and doesn’t Discover have any respect for them as loyal long-standing cardholders, and what’s that, why yes, of course, they’d be quite happy to accept Discover’s apology for this tragic error if it came with a two-for-one gift certificate to the Olive Garden.
  • Most importantly: is it considered fraud if I knowingly fill out an application for student loans in my lovely wife-daughter’s name?

large-scale data exploration, MIC-style

UPDATE 2/8/2012: Simon & Tibshirani posted a critical commentary on this paper here. See additional thoughts here.

Real-world data are messy. Relationships between two variables can take on an infinite number of forms, and while one doesn’t see, say, umbrella-shaped data very often, strange things can happen. When scientists talk about correlations or associations between variables, they’re usually referring to one very specific form of relationship–namely, a linear one. The assumption is that most associations between pairs of variables are reasonably well captured by positing that one variable increases in proportion to the other, with some added noise. In reality, of course, many associations aren’t linear, or even approximately so. For instance, many associations are cyclical (e.g., hours at work versus day of week), or curvilinear (e.g., heart attacks become precipitously more frequent past middle age), and so on.

Detecting a non-linear association is potentially just as easy as detecting a linear relationship if we know the form of that association up front. But there, of course, lies the rub: we generally don’t have strong intuitions about how most variables are likely to be non-linearly related. A more typical situation in many ‘big data’ scientific disciplines is that we have a giant dataset full of thousands or millions of observations and hundreds or thousands of variables, and we want to determine which of the many associations between different variables are potentially important–without knowing anything about their potential shape. The problem, then, is that traditional measures of association don’t work very well; they’re only likely to detect associations to the extent that those associations approximate a linear fit.

A new paper in Science by David Reshef and colleagues (and as a friend pointed out, it’s a feat in and of itself just to get a statistics paper into Science) directly targets this data mining problem by introducing an elegant new measure of association called the Maximal Information Coefficient (MIC; see also the authors’ project website).  The clever insight at the core of the paper is that one can detect a systematic (i.e., non-random) relationship between two variables by quantifying and normalizing their maximal mutual information. Mutual information (MI) is an information theory measure of how much information you have about one variable given knowledge of the other. You have high MI when you can accurately predict the level of one variable given knowledge of the other, and low MI when knowledge of one variable is unhelpful in predicting the other. Importantly, unlike other measures (e.g., the correlation coefficient), MI makes no assumptions about the form of the relationship between the variables; one can have high mutual information for non-linear associations as well as linear ones.

MI and various derivative measures have been around for a long time now; what’s innovative about the Reshef et al paper is that the authors figured out a way to efficiently estimate and normalize the maximal MI one can obtain for any two variables. The very clever approach the authors use is to overlay a series of grids on top of the data, and to keep altering the resolution of the grid and moving its lines around until one obtains the maximum possible MI. In essence, it’s like dropping a wire mesh on top of a scatterplot and playing with it until you’ve boxed in all of the data points in the most informative way possible. And the neat thing is, you can apply the technique to any kind of data at all, and capture a very broad range of systematic relationships, not just linear ones.

To give you an intuitive sense of how this works, consider this Figure from the supplemental material:

The underlying function here is sinusoidal. This is a potentially common type of association in many domains–e.g., it might explain the cyclical relationship between, say, coffee intake and hour of day (more coffee in the early morning and afternoon; less in between). But the linear correlation is essentially zero, so a typical analysis wouldn’t pick it up at all. On the other hand, the relationship itself is perfectly deterministic; if we can correctly identify the generative function in this case, we would have perfect information about Y given X. The question is how to capture this intuition algorithmically–especially given that real data are noisy.

This is where Reshef et al’s grid-based approach comes in. In the left panel above, you have a 2 x 8 grid overlaid on a sinusoidal function (the use of a 2 x 8 resolution here is just illustrative; the algorithm actually produces estimates for a wide range of grid resolutions). Even though it’s the optimal grid of that particular resolution, it still isn’t very good: knowing which row a particular point along the line falls into doesn’t tell you a whole lot about which column it falls into, and vice versa. In other words, mutual information is low. By contrast, the optimal 8 x 2 grid on the right side of the figure has a (perfect) MIC of 1: if you know which row in the grid a point on the line falls into, you can also determine which column it falls into with perfect accuracy. So the MIC approach will detect that there’s a perfectly systematic relationship between these two variables without any trouble, whereas the standard pearson correlation would be 0 (i.e., no relation at all). There are a couple of other steps involved (e.g., one needs to normalize the MIC to account for differences in grid resolution), but that’s the gist of it.

If the idea seems surprisingly simple, it is. But as with many very good ideas, hindsight is 20/20; it’s an idea that seems obvious once you hear it, but clearly wasn’t trivial to come up with (or someone would have done it a long time ago!). And of course, the simplicity of the core idea also shouldn’t blind us to the fact that there was undoubtedly a lot of very sophisticated work involved in figuring out how to normalize and bound the measure, provin that the approach works and implementing a dynamic algorithm capable of computing good MIC estimates in a reasonable amount of time (this Harvard Gazette article suggests Reshef and colleagues worked on the various problems for three years).

The utility of MIC and its improvement over existing measures is probably best captured in Figure 2 from the paper:

Panel A shows the values one obtains with different measures when trying to capture different kinds of noiseless relationships (e.g., linear, exponential, and sinusoidal ones). The key point is that MIC assigns a value of 1 (the maximum) to every kind of association, whereas no other measure is capable of detecting the same range of associations with the same degree of sensitivity (and most fail horribly). By contrast, when given random data, MIC produces a value that tends towards zero (though it’s still not quite zero, a point I’ll come back to later). So what you effectively have is a measure that, with some caveats, can capture a very broad range of associations and place them on the same metric. The latter aspect is nicely captured in Panel G, which gives one a sense of what real (i.e., noisy) data corresponding to different MIC levels would look like. The main point is that, unlike other measures, a given value can correspond to very different types of associations. Admittedly, this may be a mixed blessing, since the flip side is that knowing the MIC value tells you almost nothing about what the association actually looks like (though Anscombe’s Quartet famously demonstrates that even a linear correlation can be misleading in this respect). But on the whole, I think it represents a potentially big advance in our ability to detect novel associations in a data-driven way.

Having introduced and explained the method, Reshef et al then go on to apply it to 4 very different datasets. I’ll just focus on one here–a set of global indicators from the World Health Organization (WHO). The data set contains 357 variables, or 63,546 variable pairs. When plotting MIC against the Pearson correlation coefficient the data look like this (panel A; click to blow up the figure):

The main point to note is that while MIC detects most strong linear effects (e.g., panel D), it also detects quite a few associations that have low linear correlations (e.g., E, F, and G). Reshef et al note that many of these effects have sensible interpretations (e.g., they argue that the left trend line in panel F reflects predominantly Pacific Island nations where obesity is culturally valued, and hence increases with income), but would be completely overlooked by an automated data mining approach that focuses only on linear correlations. They go on to report a number of other interesting examples ranging from analyses of gut bacteria to baseball statistics. All in all, it’s a compelling demonstration of a new metric that could potentially play an important role in large-scale data mining analyses going forward.

That said, while the paper clearly represents an important advance for large-scale data mining efforts, it’s also quite light on caveats and limitations (even for a length-constrained Science paper). Some potential concerns that come to mind:

  • Reshef et al are understandably going to put their best foot forward, so we can expect that the ‘representative’ examples they display (e.g., the WHO scatter plots above) are among the cleanest effects in the data, and aren’t necessarily typical. There’s nothing wrong with this, but it’s worth keeping in mind that much (and perhaps most) of the time, the associations MIC identifies aren’t going to be quite so clear-cut. Reshef’s et al approach can help identify potentially interesting associations, but once they’re identified, it’s still up to the investigator to figure out how to characterize them.
  • MIC is a (potentially quite heavily) biased measure. While it’s true, as the authors suggest, that it will “tend to 0 for statistically independent variables”, in most situations, the observed value will be substantially larger than 0 even when variables are completely uncorrelated. This falls directly out of the ‘M’ in MIC, because when you take the maximal value from some larger search space as your estimate, you’re almost invariably going to end up capitalizing on chance to some degree. MIC will only tend to 0 when the sample size is very large; as this figure (from the supplemental material) shows, even with a sample size of n = 204, the MIC for uncorrelated variables will tend to hover somewhere around .15 for the parameterization used throughout the paper (the red line):
    This isn’t a huge deal, but it does mean that interpretation of small MIC values is going to be very difficult in practice, since the lower end of the distribution is going to depend heavily on sample size. And it’s quite unpleasant to have a putatively standardized metric of effect size whose interpretation depends to some extent on sample parameters.
  • Reshef et al don’t report any analyses quantifying the sensitivity of MIC compared to conventional metrics like Pearson’s correlation coefficient. Obviously, MIC can pick up on effects Pearson can’t; but a crucial question is whether MIC shows comparable sensitivity when effects are linear. Similarly, we don’t know how well MIC performs when sample sizes are substantially smaller than those Reshef et al use in their simulations and empirical analyses. If it breaks down with n’s on the order of, say, 50 – 100, that would be important to know. So it would be great to see follow-up work characterizing performance under such circumstances–preferably before a flood of papers is published that all use MIC to do data mining in relatively small data sets.
  • As Andrew Gelman points out here, it’s not entirely clear that one wants a measure that gives a high r-square-like value for pretty much any non-random association between variables. For instance, a perfect circle would get an MIC of 1 at the limit, which is potentially weird given that you can’t never deterministically predict y from x. I don’t have a strong feeling about this one way or the other, but can see why this might bother someone.

Caveats aside though, from my perspective–as someone who likes to play with very large datasets but isn’t terribly statistically savvy–the Reshef et al paper seems like a really impressive piece of work that could have a big impact on at least some kinds of data mining analyses. I’d be curious to hear what more quantitatively sophisticated folks have to say.

ResearchBlogging.org
Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, & Sabeti PC (2011). Detecting novel associations in large data sets. Science (New York, N.Y.), 334 (6062), 1518-24 PMID: 22174245

elsewhere on the net

Some neat links from the past few weeks:

  • You Are No So Smart: A celebration of self-delusion. An excellent blog by journalist David McCraney that deconstructs common myths about the way the mind works.
  • NPR has a great story by Jon Hamilton about the famous saga of Einstein’s brain and what it’s helped teach us about brain function. [via Carl Zimmer]
  • The Neuroskeptic has a characteristically excellent 1,000 word explanation of how fMRI works.
  • David Rock has an interesting post on some recent work from Baumeister’s group purportedly showing that it’s good to believe in free will (whether or not it exists). My own feeling about this is that Baumeister’s not really studying people’s philosophical views about free will, but rather a construct closely related to self-efficacy and locus of control. But it’s certainly an interesting line of research.
  • The Prodigal Academic is a great new blog about all things academic. I’ve found it particularly interesting since several of the posts so far have been about job searches and job-seeking–something I’ll be experiencing my fill of over the next few months.
  • Prof-like Substance has a great 5-part series (1, 2, 3, 4, 5) on how blogging helps him as an academic. My own (much less eloquent) thoughts on that are here.
  • Cameron Neylon makes a nice case for the development of social webs for data mining.
  • Speaking of data mining, Michael Driscoll of Dataspora has an interesting pair of posts extolling the virtues of Big Data.
  • And just to balance things out, there’s this article in the New York Times by John Allen Paulos that offers some cautionary words about the challenges of using empirical data to support policy decisions.
  • On a totally science-less note, some nifty drawings (or is that photos?) by Ben Heine (via Crooked Brains):

the perils of digging too deep

Another in a series of posts supposedly at the intersection of fiction and research methods, but mostly just an excuse to write ridiculous stories and pretend they have some sort of moral.


Dr. Rickles the postdoc looked a bit startled when I walked into his office. He was eating a cheese sandwich and watching a chimp on a motorbike on his laptop screen.

“YouTube again?” I asked.

“Yes,” he said. “It’s lunch.”

“It’s 2:30 pm,” I said, pointing to my watch.

“Still my lunch hours.”

Lunch hours for Rickles were anywhere from 11 am to 4 pm. It depended on exactly when you walked in on him doing something he wasn’t supposed to; that was the event that marked the onset of Lunch.

“Fair enough,” I said. “I just stopped by to see how things were going.”

“Oh, quite well.” said Rickles. “Things are going well. I just found a video of a chimp and a squirrel riding a motorbike together. They aren’t even wearing helmets! I’ll send you the link.”

“Please don’t. I don’t like squirrels. But I meant with work. How’s the data looking.”

He shot me a pained look, like I’d just caught him stealing video game money from his grandmother.

“The data are TERRIBLE,” he said in all capital letters.

I wasn’t terribly surprised at the revelation; I’d handed Rickles the dataset only three days prior, taking care not to  tell him it was the dataset from hell. Rickles was the fourth or fifth person in the line of succession; the data had been handed down from postdoc to graduate student to postdoc for several years now. Everyone in the lab wanted to take a crack at it when they first heard about it, and no one in the lab wanted anything to do with it once they’d taken a peek. I’d given it to Rickles in part to teach him a lesson; he’d been in the lab for several weeks now and somehow still seemed happy and self-assured.

“Haven’t found anything interesting yet?” I asked. “I thought maybe if you ran the Flimflan test on the A-trax, you might get an effect. Or maybe if you jimmied the cryptos on the Borgatron…”

“No, no,” Rickles interrupted, waved me off. “The problem isn’t that there’s nothing interesting in the data; it’s that there’s too MUCH stuff. There are too MANY results. The story is too COMPLEX.”

That didn’t compute for me, so I just stared at him blankly. No one ever found COMPLEX effects in my lab. We usually stopped once we found SIMPLE effects.

Rickles was unimpressed.

“You follow what I’m saying, Guy? There are TOO-MANY-EFFECTS. There’s too much going on in the data.”

“I don’t see how that’s possible,” I said. “Keith, Maria, and Lakshmi each spent weeks on this data and found nothing.”

“That,” said Rickles, “is because Keith, Maria, and Lakshmi never thought to apply the Epistocene Zulu transform to the data.”

The Epistocene Zulu transform! It made perfect sense when you thought about it; so why hadn’t I ever thought about it? Who was Rickles cribbing analysis notes from?

“Pull up the data,” I said excitedly. “I want to see what you’re talking about.”

“Alright, alright. Lunch hours are over now anyway.”

He grudgingly clicked on the little X on his browser. Then he pulled up a spreadsheet that must have had a million columns in it. I don’t know where they’d all come from; it had only had sixteen thousand or so when I’d had the hard drives delivered to his office.

“Here,” said Rickles, showing me the output of the Pear-sampled Tea test. “There’s the A-trax, and there’s its Nuffton index, and there’s the Zimming Range. Look at that effect. It’s bigger than the zifflon correlation Yehudah’s group reported in Nature last year.”

“Impressive,” I said, trying to look calm and collected. But in my head, I was already trying to figure out how I’d ask the department chair for a raise once this finding was published. Each point on that Zimming Range is worth at least $500, I thought.

“Are there any secondary analyses we could publish alongside that,” I asked.

“Oh, I don’t think you want to publish that,” Rickles laughed.

“Why the hell not? It could be big! You just said yourself it was a giant effect!”

“Oh sure. It’s a big effect. But I don’t believe it for one second.”

“Why not? What’s not to like? This finding make’s Yehudah’s paper look like a corn dog!”

I recognized, in the course of uttering those words, that they did not constitute the finest simile ever produced.

“Well, there are two massive outliers, for one. If you eliminate them, the effect is much smaller. And if you take into consideration the Gupta skew because the data were collected with the old reverberator, there’s nothing left at all.”

“Okay, fine,” I muttered. “Is there anything else in the data?”

“Sure, tons of things. Like, for example, there’s a statistically significant gamma reduction.”

“A gamma reduction? Are you sure? Or do you mean beta,” I asked.

“Definitely gamma,” said Rickles. “There’s nothing in the betas, deltas, or thetas. I checked.”

“Okay. That sounds potentially interesting and publishable. But I bet you’re going to tell me why we shouldn’t believe that result, either, right?”

“Well,” said Rickles, looking a bit self-conscious, “it’s just that it’s a pretty fine-grained analysis; you’re not really leaving a lot of observations when you slice it up that thin. And the weird thing about the gamma reduction is that it is essentially tantamount to accepting a null effect; this was Jayaraman’s point in that article in Statistica Splenda last month.”

“Sure, the Gerryman article, right. I read that. Forget the gamma reduction. What else?”

“There are quite a few schweizels,” Rickles offered, twisting the cap off a beer that had appeared out of the minibar under his desk.

I looked at him suspiciously. I suspected it was a trap; Rickels knew how much I loved Schweizel units. But I still couldn’t resist. I had to know.

“How many schweizels are there,” I asked, my hand clutching at the back of a nearby chair to help keep me steady.

“Fourteen,” Rickles said matter-of-factedly.

“Fourteen!” I gasped. “That’s a lot of schweizels!”

“It’s not bad,” said Rickles. “But the problem is, if you look at the B-trax, they also have a lot of schweizels. Seventeen of them, actually.”

“Seventeen schweizels!” I exclaimed. “That’s impossible! How can there be so many Schweizel units in one dataset!”

“I’m not sure. But… I can tell you that if you normalize the variables based on the Smith-Gill ratio, the effect goes away completely.”

There it was; the sound of the other shoe dropping. My heart gave a little cough–not unlike the sound your car engine makes in the morning when it’s cold and it wants you to stop provoking it and go back to bed. It was aggravating, but I understood what Rickles was saying. You couldn’t really say much about the Zimming Range unless your schweizel count was properly weighted. Still, I didn’t want to just give up on the schweizels entirely. I’d spent too much of my career delicately massaging schweizels to give up without one last tug.

“Maybe we can just say that the A-trax/Nuffton relationship is non-linear?” I suggested.

“Non-linear?” Rickles snorted. “Only if by non-linear you mean non-real! If it doesn’t survive Smith-Gill, it’s not worth reporting!”

I grudgingly conceded the point.

“What about the zifflons? Have you looked at them at all? It wouldn’t be so novel given Yehudah’s work, but we might still be able to get it into some place like Acta Ziffletica if there was an effect…”

“Tried it. There isn’t really any A-trax influence on zifflons. Or a B-trax effect, for that matter. There is a modest effect if you generate the Mish component for all the trax combined and look only at that. But that’s a lot of trax, and we’re not correcting for multiple Mishing, so I don’t really trust it…”

I saw that point too, and was now nearing despondency. Rickles had shot down all my best ideas one after the other. I wondered how I’d convince the department chair to let me keep my job.

Then it came to me in a near-blinding flash of insight. Near blinding, because I smashed my forehead on the overhead chandelier jumping out of my chair. An inch lower, and I’d have lost both eyes.

“We need to get that chandelier replaced,” I said, clutching my head in my hands. “It has no business hanging around in an office like this.”

“We need to get it replaced,” Rickles agreed. “I’ll do it tomorrow during my lunch hours.”

I knew that meant the chandelier would be there forever–or at least as long as Rickles inhabited the office.

“Have you tried counting the Dunams,” I suggested, rubbing my forehead delicately and getting back to my brilliant idea.

“No,” he said, leaning forward in his chair slightly. “I didn’t count Dunams.”

Ah-hah! I thought to myself. Not so smart are we now! The old boy’s still got some tricks up his sleeve.

“I think you should count the Dunams,” I offered sagely. “That always works for me. I do believe it might shed some light on this problem.”

“Well…” said Rickles, shaking his head slightly, “maaaaaybe. But Li published a paper in Psykometrika last year showing that Dunam counting is just a special case of Klein’s occidental protrusion method. And Klein’s method is more robust to violations of normality. So I used that. But I don’t really know how to interpret the results, because the residual is negative.”

I really had no idea either. I’d never come across a negative Dunam residual, and I’d never even heard of occidental protrusion. As far as I was concerned, it sounded like a made-up method.

“Okay,” I said, sinking back into my chair, ready to give up. “You’re right. This data… I don’t know. I don’t know what it means.”

I should have expected it, really; it was, after all, the dataset from hell. I was pretty sure my old RA had taken a quick jaunt through purgatory every morning before settling into the bench to run some experiments.

“I told you so,” said Rickles, putting his feet up on the desk and handing me a beer I didn’t ask for. “But don’t worry about it too much. I’m sure we’ll figure it out eventually. We probably just haven’t picked the right transformation yet. There’s Nordstrom, El-Kabir, inverse Zulu…”

He turned to his laptop and double-clicked an icon on the desktop that said “YouTube”.

“…or maybe you can just give the data to your new graduate student when she starts in a couple of weeks,” he said as an afterthought.

In the background, a video of a chimp and a puppy driving a Jeep started playing on a discolored laptop screen.

I mulled it over. Should I give the data to Josephine? Well, why not? She couldn’t really do any worse with it, and it would be a good way to break her will quickly.

“That’s not a bad idea, Rickles,” I said. “In fact, I think it might be the best idea you’ve had all week. Boy, that chimp is a really aggressive driver. Don’t drive angry, chimp! You’ll have an accid–ouch, that can’t be good.”

The

perils of digging too deep

Dr. Rickles the postdoc looked a bit startled when I walked into his office. He was eating a cheese sandwich and watching a chimp on a motorbike on his laptop screen.
“YouTube again?” I asked.
“Yes,” he said. “It’s lunch.”
“It’s 2:30 pm,” I said, pointing to my watch.
“Still my lunch hours.”
Lunch hours for Rickles were anywhere from 11 am to 4 pm. It depended on exactly when you walked in on him doing something he wasn’t supposed to; that was the event that marked the onset of Lunch.
“Fair enough,” I said. “I just stopped by to see how things were going.”
“Oh, quite well.” said Rickles. “Things are going well. I just found a video of a chimp and a squirrel riding a motorbike together. They aren’t even wearing helmets! I’ll send you the link.”
“Please don’t. I don’t like squirrels. But I meant with work. How’s the data looking.”
He shot me a pained look, like I’d just caught him stealing video game money from his grandmother.
“The data are TERRIBLE,” he said in all capital letters.
I wasn’t terribly surprised at that revelation; I’d handed Rickles the dataset only three days prior, taking care not to  tell him it was the dataset from hell. Rickles was the fourth or fifth person in the line of succession; the data had been handed down from postdoc to graduate student to postdoc for several years now. Everyone in the lab wanted to take a crack at it when they first heard about it, and no one in the lab wanted anything to do with it once they’d taken a peek. I’d given it to Rickles in part to teach him a lesson; he’d been in the lab for several weeks now and somehow still seemed happy and self-assured.
“Haven’t found anything interesting yet?” I asked. “I thought maybe if you ran the Flimflan test on the A-trax, you might get an effect. Or maybe if you jimmied the cryptos on the Borgatron…”
“No, no,” Rickles interrupted, waved me off. “The problem isn’t that there’s nothing interesting in the data; it’s that there’s too MUCH stuff. There are too MANY results. The story is too COMPLEX.”
That didn’t compute for me, so I just stared at him blankly. No one ever found COMPLEX effects in my lab. We usually stopped once we found SIMPLE effects.
Rickles was unimpressed.
“You follow what I’m saying, Guy? There are TOO-MANY-EFFECTS. There’s too much going on in the data.”
“I don’t see how that’s possible,” I said. “Keith, Maria, and Lakshmi each spent weeks on this data and found *nothing*.”
“That,” said Rickles, “is because Keith, Maria, and Lakshmi never thought to apply the Epistocene Zulu transform to the data.”
The Epistocene Zulu transform! It made perfect sense when you thought about it; so why hadn’t I ever thought about it? Who was Rickles cribbing analysis notes from?
“Pull up the data,” I said excitedly. “I want to see what you’re talking about.”
“Alright, alright. Lunch hours are over now anyway.”
He grudgingly clicked on the little X on his browser. Then he pulled up a spreadsheet that must have had a million columns in it. I don’t know where they’d all come from; it had only had sixteen thousand or so when I’d had the hard drives delivered to his office.
“Here,” said Rickles, showing me the output of the Pear-sampled Tea test. “There’s the A-trax, and there’s its Nuffton index, and there’s the Zimming Range. Look at that effect. It’s bigger than the zifflon correlation Yehudah’s group reported in Nature last year.”
“Impressive,” I said, trying to look calm and collected. But in my head, I was already trying to figure out how I’d ask the department chair for a raise once this finding was published. *Each point on that Zimming Range is worth at least $500*, I thought.
“Are there any secondary analyses we could publish alongside that,” I asked.
“Oh, I don’t think you want to publish *that*,” Rickles laughed.
“Why the hell not? It could be big! You just said yourself it was a giant effect!”
“Oh *sure*. It’s a big effect. But I don’t believe it for one second.”
“Why not? What’s not to like? This finding make’s Yehudah’s paper look like a corn dog!”
I recognized, in the course of uttering those words, that they did not constitute the finest simile ever.
“Well, there are two massive outliers, for one. If you eliminate them, the effect is much smaller. And if you take into consideration the Gupta skew because the data were collected with the old reverberator, there’s nothing left at all.”
“Okay, fine,” I muttered. “Is there anything else in the data?”
“Sure, tons of things. Like, for example, there’s a statistically significant Gamma reduction.”
“A gamma reduction? Are you sure? Or do you mean Beta,” I asked.
“Definitely gamma,” said Rickles. “There’s nothing in the betas, deltas, or thetas. I looked.”
“Okay. That sounds potentially interesting and publishable. But I bet you’re going to tell me why we shouldn’t believe that result, either, right?”
“Well,” said Rickles, looking a bit self-conscious, “it’s just that it’s a pretty fine-grained analysis; you’re not really leaving a lot of observations when you slice it up that thin. And the weird thing about the gamma reduction is that it is essentially tantamount to accepting a null effect; this was Jayaraman’s point in that article in *Statistica Splenda* last month.”
“Sure, the Gerryman article, right. Okay. Forget the gamma reduction. What else?”
“There are quite a few Schweizels,” Rickles offered, twisting the cap off a beer that had appeared out of the minibar under his desk.
I looked at him suspiciously. I suspected it was a trap; Rickels knew how much I loved Schweizel units. But I still couldn’t resist. I had to know.
“How many Schweizels are there,” I asked, my hand clutching at the back of a nearby chair to help me stay upright.
“Fourteen,” Rickles said matter-of-factedly.
“Fourteen!” I gasped. “That’s a lot of Schweizels!”
“It’s not bad,” said Rickles. “But the problem is, if you look at the B-trax, they also have a lot of Schweizels. Seventeen of them, actually.”
“Seventeen Schweizels!” I exclaimed. “That’s impossible! How can there be so many Schweizel units in one dataset!”
“I’m not sure. But… I can tell you that if you normalize the variables based on the Smith-Gill ratio, the effect goes away completely.”
There it was; the sound of the other shoe dropping. My heart gave a little cough–not unlike the sound your car engine makes in the morning when it’s cold and it wants you to go back to bed and stop stressing it out. It was aggravating, but I understood what Rickles was saying. You couldn’t really say much about the Zimming Range unless your Schweizel count was properly weighted. Still, I didn’t want to just give up on the Schweizels entirely.
“Maybe we can just say that the A-trax/Nuffton relationship is non-linear,” I proposed.
“Non-linear?” Rickles snorted. “Only if by non-linear you mean non-real! If it doesn’t survive Smith-Gill, it’s not worth reporting!”
I grudgingly conceded the point.
“What about the zifflons? Have you looked at them at all? It wouldn’t be so novel given Yehudah’s work, but we might still be able to get it into some place like *Acta Ziffletica* if there was an effect…”
“Tried it. There isn’t really any A-trax influence on zifflons. Or a B-trax effect, for that matter. There *is* a modest effect if you generate the Mish component for all the trax combined and look only at that. But that’s a lot of trax, and we’re not correcting for multiple Mishing, so I don’t really trust it…”
I saw that point too, and was now nearing despondency. Rickles had shot down all my best ideas one after the other. What else was left?
Then it came to me in a near-blinding flash of insight. *Near* blinding, because I smashed my forehead on the overhead chandelier jumping out of my chair. An inch lower, and I’d have lost both eyes.
“We need to get that chandelier replaced,” I said, clutching my head in my hands. “It has no business hanging around in an office like this.”
“We need to get it replaced,” Rickles agreed. “I’ll do it tomorrow during my lunch hours.”
I knew that meant the chandelier would be there forever–or at least as long as Rickles inhabited the office.
“Have you tried counting the Dunams,” I suggested, rubbing my forehead delicately and getting back to my brilliant idea.
“No,” he said, leaning forward in his chair slightly. “I didn’t count Dunams.”
Ah-hah! I thought to myself. Not so smart are we now! The old boy’s still got some tricks up his sleeve.
“I think you should count the Dunams,” I offered sagely. “That always works for me. I do believe it might shed some light on this problem.”
“Well…” said Rickles, shaking his head slightly, “maaaaaybe. But Li published a paper in Psychometrika last year showing that Dunam counting is just a special case of Klein’s occidental protrusion method. And Klein’s method is more robust to violations of normality. So I used that. But I don’t really know how to interpret the results, because the residual is *negative*.”
I really had no idea either. I’d never come across a negative Dunam residual, and I’d never even heard of occidental protrusion. As far as I was concerned, it sounded like a made-up method.
“Okay,” I said, sinking back into my chair, ready to give up. “You’re right. This data… I don’t know. I don’t know what it means.” I should have expected it, really; it was, after all, the dataset from hell. I was pretty sure my old RA had collected it after taking a quick jaunt through purgatory every morning.
“I told you so,” said Rickles, putting his feet up on the desk and handing me a beer I didn’t ask for. “But don’t worry about it too much. I’m sure we’ll figure it out eventually. We probably just haven’t picked the right transformation yet.”
He turned to his laptop and double-clicked an icon on the desktop that said “YouTube”.
“Maybe you can give the data to your new graduate student when she starts in a couple of weeks,” he said as an afterthought.
In the background, a video of a chimp and a puppy driving a Jeep started playing on a discolored laptop screen.
I mulled it over. Should I give the data to Josephine? Well, why not? She couldn’t really do any *worse* with it, and it *would* be a good way to break her will in a hurry.
“That’s not a bad idea, Rickles,” I said. “In fact, I think it might be the best idea you’ve had all week. Boy, that chimp is a really aggressive driver. Don’t drive angry, chimp! You’ll have an accid–ouch, that can’t be good.”