You mean the statistics demonstration? I'm sure I've seen it in several places.
I know of two tricks for detecting the students. The first is to look for six or seven heads or tails in a row. Over a hundred tosses, a coin will probably do that, but humans "being random" won't. The other is to look at the page as a sequence of "HHH" and "TT" strings and estimate how many there are. A coin, of course, changes from heads to tails 50% of the time, but a human does it more like 70% of the time.
I'm sure there are other characteristics, too, but those two are sufficient to throw out most human attempts at a glance. It's actually kind of obvious, when you see the two side by side.
Me "being random" with the numpad: 10110101001010010101011010100101011010100101010110101
Cool! Makes sense, too— it feels unrandom to sit there hitting one key a bunch. How do you know when to stop?
So here's what I got: figure I have a bias to switch keys. That means that 01 and 10 are more common than 11 and 00. So what I need to do is group 01 with 00, and 10 with 11. What I do is generate twice as many bits as I need, treat the string as a sequence of two-bit pairs, and reduce each one to its first bit.
That looks like this: 01011110000010111000010011111011111111000000111010110001111000111100111100001100011010011101
Gets you past the litmus test, but looks like it goes too far the other way? Hard for me to tell, actually.
Another thing would be something like a sequential xor of each bit in triples (i.e. 010 -> (0 ^ 1) ^ 0 = 1), which segments triples across probability like so:
You can do that quickly by counting the 1s— 1 or 3 is 1, 0 or 2 is 0.
That looks like this:
00011011011011000000111111001011000101010000100100110011011010
I don't know if it adds much more (apparent) entropy, though.
It sounds very interesting. I've got to try it sometime :).