You misunderstand the statistics. A difference can be statistically significant with a trivial effect size, as here. p measures significance, r measures effect size. Here the numbers indicate that the difference is reliable (though p=.05 isn't significant where I come from especially in view of the huge sample size) but negligible.

Since you seem like that kind of guy I refer you to http://en.wikipedia.org/wiki/Effect_size#Pearson_r_.28correlation.29 so you can take Cohen's word for it instead of the author's and mine.

http://www.amazon.com/Mind-Games-Guide-Inner-Space/dp/0440556341/ ?

He's not "Dr" Wakefield. He's just Andrew Wakefield, fraudster and disgrace to science. From the article: "On 24 May 2010, the GMC panel found Wakefield guilty of serious professional misconduct on four counts of dishonesty and 12 involving the abuse of developmentally challenged children, and ordered that he be struck off the medical register."

Yes they know about the hidden partition. What's worse is that they can't prove it's there, but you can't prove it's not there. They will rubber-hose you until you give up the secret partition. If you don't actually have a secret partition to give up, they'll rubber-hose you forever.

Why are you lot the only people in the world entitled to privacy?

A lot of people load their JQuery libraries or whatnot from a CDN. In fact I think that's the preferred behaviour. There are multiple CDNs so the list is a bit longer and more annoying than you'd think.

Some links for background:
http://encosia.com/3-reasons-why-you-should-let-google-host-jquery-for-you/
http://royal.pingdom.com/2012/07/24/best-cdn-for-jquery-in-2012/

If someone has an axe to grind, it's easy to make graphs lie. Lying with inferential statistics is a bit more involved, so that gets held off until third year. (No joke, I had a research methods assignment that required me to formulate conclusions then generate a dataset to support them.) But when you legitimately want to understand what's in data, or explain it honestly to someone else, the right visualisation beats statistics every time. Humans are pattern detectors, graphs contain patterns, statistics are just numbers. If you can't see a finding in the visualisation, and you have to rely on a statistical test to demonstrate it, there's a high risk that it's not really there.

This is oddly reassuring. I'm a reasonably smart guy, a strong statistician and programmer, and I cannot follow formulas. They're not intuitive and I have to painstakingly work through and translate them as if they were a foreign alphabet. I sometimes wonder whether that's just me, but it seems not. I note that someone in TFA's comments posted a mathematical explanation to demonstrate how much clearer it made things, and at the first Greek letter I skipped to the next comment. No doubt everyone in the 'general audience' would do just the same.

Radiohead played in Osaka in 2008. I flew from Australia to hear them, so I know all about inconvenient concert venues...

Do you have evidence to support any of your opinions? People have been looking at the relationship between applicant selection methods and job performance for as long as statistical tests have been around, and there are no compellingly good methods. People think it's easy until we go to the data and see how their preferred methods worked out objectively over a decent number of hires. Have you done this? How many people have you hired, how was their performance measured, how did they work out?

That would be the whole thrust of the article actually (although the stuff about Google's process being ineffective is all from 2009 which makes it quite old news): Google thought that gauntlet interviewing using methods quite similar to what you're advocating was giving them the world's best employees, because it felt like something that would be effective... but measurement showed that it wasn't.

It's been known to organisational psychologists for ages that the least-worst method is to observe candidates' performance in the actual job environment or a good simulation of it for a week or two. From reading the article and some of the things linked from it, it seems like IT hirers have begun to work this out independently.

I like it when people who make claims provide citations so here's a good entry point to the research: http://mavweb.mnsu.edu/howard/Schmidt%20and%20Hunter%201998%20Validity%20and%20Utility%20Psychological%20Bulletin.pdf

An indicator for M/F isn't recording anything much about genetic sex. If that's what you're setting out to do you'll need a much bigger box: http://en.wikipedia.org/wiki/Sex_chromosome_disorders

Even for people with standard-issue XX or XY sex chromosomes, the journey from that to phenotypical gender is about a six-stage process. Most people arrive at one of two endpoints, but that still leaves another 62 or so different bit-patterns for phenotypical gender, and as the article suggests the low-order bits can be flipped after birth. A write-only boolean field doesn't really do the job.

No doubt they would all simply pair off and each invoice the other half a million dollars for an executive backrub, the fair market value of which is established by what the other guy charges?

Check out http://learn.adafruit.com/reading-a-analog-in-and-controlling-audio-volume-with-the-raspberry-pi/overview which I happened to be reading about 5 minutes ago. I'm not familiar with the One-Wire protocol but you can certainly bit-bang good ole analog sensors on the Pi. In Python, even.

So for those of us who currently don't get the joke, but are always willing to learn something, what's the punchline? Is it that one gun looks scarier but they are functionally the same?

I used to have a huge honkin rubber protective case. A few months ago I decided to live a little, and my iPhone was stripped of its rubber outfit and screen protector. It's glorious. Thin, clear display, stylish. No cracks or scratches on either surface yet. One drop onto anything but carpet and it'll be history, but honestly a year's use of a wonderful piece of design beats three years' use of some rubber lump too fat to even fit into a pocket.