stevenbedrick

Break a leg! I defended mine last month, and let me tell you- the sense of relief afterwards is amazing.

The word you're looking for might be "metacognition" ("thinking about thinking"), and I agree that it's a fascinating concept. We spend our whole lives locked inside our skulls looking out, and it can be fascinating to imagine or learn about how others experience their cognitive worlds.

To me, that's one thing I enjoy about teaching- a lot of the time, you're having to try and figure out how a student is perceiving the subject or lecture. In essence, you're constantly reverse-engineering your students' perceptions of what you're telling them.

Huh. OK, I can edit this comment's parent comment ("Ah, thanks!...") but not my original comment. Is there something going on whereby comments older than a certain number of hours can't be edited?

You know, that's what I thought for the longest time, but I eventually saw enough otherwise-perfectly-normal adults get hung up on the same problem--- and talked to enough other teachers who reported the same thing--- that I came to believe that there was something fundamentally different going on, and that it wasn't just a few tough students.

As somebody who's been programming for most of my life, it was really hard for me to learn to think about this stuff from the perspective of somebody who'd never seen it before. Many thought patterns and cognitive skills that are second nature to us programmers are, for a lot of people, very difficult to learn.

Ah, thanks! I'd been trying to figure out how to do that. I missed the memo on how to use HN's comment formatting system. Now, if I could just figure out how to edit a comment...

I've said it before, and I'll say it again. In my experience, the problems that students have ultimately fall into one of two categories:

1. Learning to think algorithmically; 2. Abstraction.

What do I mean by these? By "thinking algorithmically," I really mean "taking a large problem and breaking it up into an ordered series of steps or sub-problems." This is surprisingly hard for a lot of people, in part because the correct "size" of steps or sub-problem can change from problem to problem and from language to language. This can absolutely be taught, though. Some students have an easier time of this than others, but eventually (in my experience) they all get there in the end, and develop an intuitive sense of how to do it... at which point 99% of people immediately "forget" what it was like to not be able to do it, and become incapable of recognizing and dealing with students who haven't gotten there yet. :-) Most programmers (myself included, at least until my dozenth or so student, at which point I figured out what was going on) fall into this category: thinking in this manner is so deeply imprinted into our cognitive processes that we typically have a very difficult time visualizing what it's like to not be there. We're sort of like fish and water, if you know what I mean.

The second problem is much more difficult to address. Abstraction is one of those things that, I'm convinced, some (most) people can "get" and others simply can't. For example, I've had students who seemed to understand arrays perfectly, including the idea of using an integer subscript to refer to the value at an array index such that if you showed them the following code:

<pre>

myList = ['monkeys', 'elephants', 'giraffes'] print myList[1]

</pre>

they'd easily tell you that it would print 'elephants' (yes, they can usually get their heads around zero-indexing without issues). If, however, you showed them this code:

<pre>

myList = ['monkeys', 'elephants', 'giraffes'] idx = 1 print myList[idx]

</pre>

a lot of them would be more than a little bit lost. Even worse would be if you showed them this code:

<pre>

for i in [0, 1, 2] print myList[i]

</pre>

They'd be really lost, then. Even if you walk through it line by line, with lots of extraneous print statements, there is a certain sub-type of (otherwise perfectly capable) student that just isn't gonna get it. I'm convinced that this problem--- lack of abstraction ability--- is also why so many students have problems with loops. Dealing with loop counter variables (and the things that you usually want to do with them--- array indexing, printing, doing math, etc.) is very hard if your brain isn't able to think in this abstract manner.

It's worse in Java than it is in Python, just 'cause there's so much more code involved (meaning that it's harder to pinpoint exactly where the confusion is coming from), but the problem totally persists even in sane teaching languages. I've talked to a lot of other CS educators who've noticed the same thing- no matter how you approach it, this level of abstraction is just beyond some students' reach. It gets worse once you try and teach them about object/class distinctions, and so on. It's not that they're stupid- my students are usually MDs working on their masters' degrees. It's just that some people's brains don't work that way. And it's not that they can't do other computer stuff- some of these students have gone on to become pretty good at SQL and even relational data modeling, which you'd think would use some of the same cognitive patterns, but apparently is different enough that it's doable.

I don't know if the problem would be better or worse teaching with a purely functional language; my gut says "worse," but I haven't tried it so I don't know, and the fact that many students with this problem go on to handle declarative languages pretty well gives me pause.

Come on, it's a cell phone carrier. There's only one possible answer to the "will XYZ cost more..." question: yes.

You don't. Many journals' PDF-downloading systems embed metadata as well as visible watermarks containing your institution and often a timestamp and an IP address (of course, if your institution uses EZProxy, the IP address might not be immediately trackable back to you).

"It's not just a job... it's an indenture!"

I actually agree 99% with you- I happily use TM more or less constantly, and have since it was first released. That said, there are four features (or feature areas) that I feel like would add immeasurably to my TextMate experience:

1. Split-window editing 2. Multithreaded searching, pasting, etc.- having the whole app hang while doing a "find in project" is a major drag, especially when it's due to, oh, say, having a bunch of files open from a slow network share. Which brings me to... 3. Better handling of files opened from remote volumes- another case where multiple threads would probably help. 4. Better handling of really long lines when word-wrap is turned off. There is obviously something O(n) going on at the line level (where n is the number of characters on the line), and it makes handling long runs of text rather painful at times.

So, there it is: my TextMate wish list. Modulo those four things, TextMate is currently pretty close to ideal, IMHO. I love its syntax highlighting grammar, its snippets, its macro capabilities, etc. etc. The Latex package is fabulous, and has singlehandedly done a lot to make my dissertation-writing process bearable.

Or, get an eReader and strip the DRM from the books you do buy. Depending on where you live, this may or may not be legal, but it's absolutely an option.

Based on some of the replies to my comment, I think people might have misunderstood what I was getting at. The main purpose of my comment was to say simply that Bloomberg could have made an equivalent rhetorical point in a way that didn't make him sound like a plutocrat arguing that he needed illegal immigrants in order to keep his golf club maintenance fees low.

Also, there's an important difference (to me, anyway) between 1) a descriptive statement about who does certain kinds of work in today's US, and 2) a prescriptive statement about who I think ought to be doing those kinds of work, and under what conditions they ought to be doing so.

Dave Barry had a great line years ago; something to the effect of "a boat will end up costing you twice as much as you planned to spend, and you'll end up enjoying it half as much as you thought you would".

Indeed, but it's still a fair question, just framed badly. What he should have said was "who works in every kitchen in every diner, and who maintains every public green space, and who builds every house?"

I like that idea quite a bit, actually- I'm envisioning a sort of "heat map" UI, where each entry in a directory listing's background color is determined by its commit frequency.

This might be true in some fields, but there was plenty of government research pre-WW2 in epidemiology and public health. Look up Walter Reed in the Wikipedia for an example of what I'm talking about.

Of course, it should be mentioned that much of that pre-WW2 government research was a) actually sponsored by the military, or by government entities that had strong military roots, and b) was of questionable ethical quality by today's standards.

Not to mention that the vast majority of Github repositories don't have enough traffic to properly build such a graph.

Thanks for the correction. I am shocked- shocked, I say!- to learn that the Wikipedia contained an incorrect datum! ;-)

Best wishes with getting the treatment you need, and for a speedy recovery afterwards (both medically and financially).

According to the Wikipedia, Draper was born in 1944, which mean that he ought to be covered by Medicare, which is the federally-run insurance program for individuals over 65. However, as other commenters have noted, even with insurance, it's not uncommon to find out that a particular procedure isn't covered by one's plan, and even very good insurance policies often don't cover costs 100%. For example, very good private insurance policies often only pay 80% of inpatient costs- the other 20% will be billed to the patient. That said, there's usually some sort of upper limit to the patient's financial responsibility, after which the insurance company pays 100%... but that limit can sometimes be in the tens of thousands of dollars, depending on the plan.

Furthermore, many insurance plans will cover a surgical procedure at one rate, but will cover ancillary costs (such as anesthesia, follow-up care, and medications) at a different (often lower) rate. This is all a long-winded way of saying that it is quite common for major medical procedures that are nominally "covered" by one's health insurance to end up costing the patient quite a bit of money when all is said and done. Look at the stats on medical bankruptcies in the US over the last few years- most of those people had insurance, and still ended up screwed.

Also, I'd like to point out that if Draper had been born a few years later (i.e., if he was younger than 65 years old) it seems quite likely that he might not have had any insurance at all. Many older people- especially people whose careers haven't been a stead string of stable corporate jobs, but have instead been independent contractors or otherwise self-employed- find themselves in the position of being 55 or 60 years old and suddenly unable to afford (or even be allowed to purchase) health insurance. Also, there are also quite a few people (especially these days) who are laid off from what had been formerly stable corporate jobs around that age, and find themselves uninsured for the first time in their lives just as their bodies are starting to break down. I know a number of people who celebrated their 65th birthday by going to the doctor for the first time in a few years.

That being sort of the whole point of VFINX... :-)

I disagree. That sort of slide (and its associated comment) is absolutely sexual harassment, in that it automatically sexualizes what is supposed to be a professional environment. Given the technology world's long and unpleasant track record of sexist behavior towards women (as well as towards... well, pretty much anybody who isn't, cisgendered, heterosexual, and male), I personally can think of very few ways that sexualizing a public technology presentation can do anything other than create (or contribute to) a hostile environment for a good chunk of the audience.

Furthermore, that sort of slide sends a clear message to the audience about who the speaker thinks they're speaking to. In this case, it says that the speaker thinks that his audience is composed of heterosexual males[1], which <sarcasm>I'm sure feels just great to any women who might also be in the audience.</sarcasm>

[1]: I'm going to go out on a limb and assume that the author of those conference slides did not include them in an attempt to appeal to the audience's lesbian members. I am, of course, willing to be corrected on this point.

I'm reminded of the scene in "The Lives of Others" where the Stasi typewriter guy is giving a presentation in which he details exactly what model and make of typewriter every writer and journalist in East Berlin uses.

Calibre can't do the conversion, but AZW files are (most of the time) just MOBI files that have been DRM'd, so if you can break the DRM, you can easily convert it to something else (ePub, etc.) using Calibre. Fortunately, the Kindle DRM has been broken for quite some time, so there are plenty of ways to do this.

Anybody else bothered by the lack of p-values on these tables? Kudos to the authors for doing some research to see whether there are differences between the performance of the various landing page and banner designs... but the way they're reported their results leaves a lot to be desired.

The the stated differences (in terms of mean donation as well as conversion rate) between banners and landing pages strike me as pretty small (e.g., $26.92 vs. $27.07)- confidence intervals would be helpful here, as I suspect that the differences aren't significant.

Also, is mean really the right metric to be reporting? I suspect that these data aren't normally distributed, or, at the very least, have some outliers on either side of the spectrum (some people who donated hardly anything, plus a few "high rollers"), so it seems to me that the median would be a more informative statistic.

I've been using Mathematica (MMA) for my dissertation's data analysis, and for the most part it's been great. As an environment to manipulate data in, it's by far the best that I've used- once you get the hang of it, the pattern/transformation-rule language is incredibly useful for reformatting, recoding, mixing, slicing, dicing, etc. one's data. If you're coming from Haskell, you'll probably pick this part up way faster than I did at first.

If your stats needs are relatively simple- linear models, glms, logit models, anovas, simple tests of hypotheses, etc.- MMA is more than adequate. The new version looks like it adds some non-parametric stats functions, as well as paired t-tests, both of which would be quite useful to me.

Also, the visualization tool in MMA are fabulous, and don't make me want to tear my beard out every time I have to go off the beaten path (as opposed to those found in certain other one-letter-long stats environments I could name). 'Nuff said. Another thing I really appreciate about MMA is how consistent the syntax and functions are- once you've figured out one function, the odds are good that your knowledge will be useful on the next function you try and figure out. This, again, stands in stark contrast to other packages (R, SAS, I'm looking at you guys).

I have found myself turning to R for certain specific things, though. Mixed-effects models, repeated-measure ANOVA, Fisher's Exact Test, etc. Really, the two work together well- it's easy to use MMA to get your data in exactly the right form for R, export it, and then do whatever you need from there.

SPSS is quite good, for certain things. A lot of researchers use it because little-to-no programming is required, and you can interact with it in an entirely GUI-way- if you can use Excel, you can use SPSS. It makes it easy to set up certain analyses, and gives lots of output... and there's where my concerns about it come up. It's easy to fall into a false sense of security with it, and to end up with statistics that you don't know how to interpret properly (I call this the "Huh. Now what do I do?" problem). The documentation is often pretty useless on this front as well- lots of pages follow this general pattern: "Jones Test of Gronkularity: If checked, SPSS will calculate the Jones Test of Gronkularity statistic, which tests the null hypothesis that the data are gronkular", as opposed to useful information about why you might care whether the data are gronkular or not, why the Jones test was included in another test's output, etc. For a product aimed at people with relatively limited technical capabilities, I feel like SPSS should have better docs.

One important thing to know about SPSS- that a lot of people don't- is that is really a programming language, for which the GUI is simply a code generator. I find that it's almost always easier for me to interact directly with the under-the-hood guts of SPSS than the GUI, although sometimes when setting up a new analysis for the first time I'll use the GUI to do most of the work and then tweak its results.

Yeah, in my experience, most biostatisticians (especially those involved in public health and clinical research) are SAS folks. Some of that is inertia- a lot of these people learned SAS at the same time they were learning stats. However, I think that most of SAS's continuing prevalence is due to the fact that, for all of its (many, many, many) problems, SAS is a freakin' log chipper when it comes to statistics- it doesn't care how much data you throw at it, or what kinds of crazy and/or exotic statistics you ask it for- if you can decipher its syntax, you can get it to do it.

Even for stuff that a lot of other programs can do just fine, SAS often has an edge. For example, everybody and their brother can do a logistic regression model... but SAS can give you confidence intervals for all kinds of crazy parts of the model that SPSS won't even bother calculating and that R will only give you point estimates for.

The other great thing about SAS is that a lot of the good statistics books from the last twenty or thirty years include SAS sample code- for example, I'm currently having to do some off-the-beaten-path ANOVA stuff, and the reference I'm using (Edwards' "Analysis of Variance for the Behavioral Sciences") uses SAS as its language of choice.

That said, I personally find the SAS "language" to be alternatively bewildering and nostalgia-inducing (the "cards" command, anybody?). SAS is the only language about which I can honestly say "it makes R's syntax look clean and predictable". Also, the Windows version of SAS is an absolute abomination from a UI standpoint. And, their licensing schemes are draconian, and installing the damn thing can easily take an entire day, especially if (say, for example) the installer gets confused because you've already got a JDK installed on your computer. Not that I'm bitter, or anything...

Of course, as others have noted, in bioinformatics, R either is already the default or is almost there. I know that in my department's bioinformatics courses, they use R, Python, and Perl almost exclusively, and only break out the SAS when there's something specific they need it to do.

This. This, this this. That is all.

The wording was a little bit unclear to me, but I interpreted it to mean that, in addition to the raise, they're changing how their bonuses are delivered- rather than as one lump sum, they're being spread out over each paycheck. I could be mis-reading, though.

They were all thwarted... but the water and cargo bombs were thwarted by intelligence work, not by security screening, and the underwear guy really should have been.