181–190 of 361 posts
Earlier quoted context omitted.
I suspect many of the “bad” responses are smart aleks saying “11111111” is just as likely a “62536164”.
Is 11111111 less likely than another sequence?
If you're forced to pick random numbers between 1 and X in your head, pick instead from a wider range of numbers and then modulo X. Your brain will legitimately have no idea what number you're picking. e.g. for a range 1-6, pick from 100-250 instead and modulo 6 plus 1. There are of course brand new biases at play (is your new range cleanly divisible by X?) But it's enough to tamp down the original biases you're worr…
Earlier quoted context omitted.
I suspect many of the “bad” responses are smart aleks saying “11111111” is just as likely a “62536164”.
Is 11111111 less likely than another sequence?
But what if we say, we're going to generate that binary sequence by 8 successive flips of a coin, and we are aiming for 11111111 specifically? Then we have to multiply eight times. (0.5)^8 == .00390625 == 1/256
Where it gets a bit tricky is if we ask people to place bets after each successive flip of the coin. For example, starting with no flips, ask players to bet on the likelihood of 8 heads in a row. Next round, bet on the likelihood of 7 heads in a row, knowing the first was a head, etc. What minimal odds should the house give after each flip in order to reliably turn a profit on this game? Does it matter how many players are at the table when it comes to calculating those odds?
I agree with the article that the study is flawed in its unwillingness to exclude the all-H and all-T answers. But I’ll go further: the original study is just silly. “Make a sequence that looks random” is sort of a nonsensical ask. Looks random to whom? To our algorithm, is what they meant. There’s no such thing as a randomness test that can look at a sequence and decide “is it random?”, so this algo measures somethi…
Probably why I got an "age" of 60.
So I do not know what they think about the test, I would have expected the utility I wrote to come up with different results than I did.
The reason the trend line drops over 60 is that there are fewer people over 60 taking the test and thus, the questionable responses have a bigger influence. If you sample less from that cohort, it will be less likely that your sample is representative.
Earlier quoted context omitted.
I would; would you? The Gambler's Paradox says you shouldn't (I'm assuming your 20-sided die isn't crooked). Incidentally, you haven't made your case that you can ever tell whether a given sequence was produced by a fair die. You've just asserted it, and then suggested a game that doesn't illuminate anything.
It would be incredibly stupid to take the bet, as it's way more likely that the sequence was not produced by a fair die than that it was (i.e. the dice is rigged in the example) Just because it's impossible to know for certain, doesn't mean you can't make a prediction with very high chance of being correct.
So I'd still expect a running straight to be rare, even with a crooked die.
Of course, if I watched the die produce 7 20s in a row, and was then asked to bet on the next roll NOT being 20, I'd be stupid to assume the die was fair without inspecting it.
All this is beside the point; the instructions invite the subject to produce a sequence that they think will convince people it was produced by a roll of dice. But there is no sequence that SHOULD have that power to convince.
> so that if another person is shown your sequence of digits from 1 to 6, he/she should not be able to tell whether these numbers were produced by a real die or just “made up” by somebody. That instruction is a flaw in the experiment. It's always impossible to tell, for any given sequence, whether it was produced by a fair die. There's nothing an experimental subject can do to make the impossible more impossible. > t…
When you throw a coin 100 times, each sequence you get is equally likely. However. You can look at properties of the sequence which are more likely to be one way than the other. For instance, it's more likely that the number of heads and tails are about equal than not. The reason is that there are more sequences, in general, where that is true, than those where heads or tails strongly prevail.
With the right property, you can make statements such as: This sequence is statistically likelier to be human made than random.
One such property is for instance the number of changes from heads to tails, or vice versa. In expectation, random sequences change heads to tails about 50% of all flips. For humans, the expectation is much higher. Hence, if you compare two sequences where one has 51% changes and one 63%, it is mathematically (/statistically) accurate to say that the latter one is likelier human made.
Earlier quoted context omitted.
I think you'd be better off taking a small pinch of sand, salt, pepper etc., throwing that on a smooth surface, then counting all the grains and then modulo it (just have the number of grains be >> than the range as in your example). This would reduce a lot of inherent biases, although perhaps introduce others. Reminds me of the Buttered Toast Ig Noble Prize: https://gizmodo.com/an-experiment-that-solves-the-worlds-m…
At what point do we draw the line and say that these methods are sufficiently random? The task at hand is to come up with a sequence you perceive as random based on the numbers themselves, so adding layers like this seems to go against the concept of the experiment entirely. How is this different from opening up my JavaScript console and doing Math.random() several times?
https://www.nature.com/articles/s41598-021-95388-7
> "The presented above examples clearly show that classic random number generators may be exposed to various attacks, or may have the so-called backdoors. This justifies the need to develop alternative technologies that could replace the classic generators on a large scale. The most promising, because they have a fundamental justification for the randomness in the formalism of quantum mechanics, are quantum random number generators."