I’m not exactly sure what you mean by “as random.”
It may well be that there are discernable patterns in a sequence of manually simulated coin-flips that would allow us to distinguish such sequences from actual coinflips. The most plausible hypothetical examples I can come up with would result in a non-1:1 ratio… e.g., humans having a bias in favor of heads or tails.
Or, if each person is laying a coin down next to the previous coin, such that they are able to see the pattern thus far, we might find any number of pattern-level biases… e.g., if told to simulate randomness, humans might be less than 50% likely to select heads if they see a series of heads-up coins, whereas if not told to do so, they might be more than 50%.
It’s kind of an interesting question, actually. I know there’s been some work on detecting test scores by looking for artificial-pattern markers in the distribution of numbers, but I don’t know if anyone’s done equivalent things for coinflips.
Thank you. I realized, as soon as I posted it, that the method of obtaining the sequence would not matter (as the previous commenter rightly said), but somehow, the ‘feeling of a question’ remained. I was not thinking of showing them part of ‘the sequence so far’… But it might be fun to determine whether there is any effect on the subject’s choice of knowing this ‘flip’ is a part of a pattern (or not knowing it), of the composition of the revealed pattern, and maybe—if there is an effect—the length of the washout period...
I mean, it’s only a coin flip! The preceding choices should have no bearing on it. It’s, like, the least significant choice you can ever make...
I’m not exactly sure what you mean by “as random.”
It may well be that there are discernable patterns in a sequence of manually simulated coin-flips that would allow us to distinguish such sequences from actual coinflips. The most plausible hypothetical examples I can come up with would result in a non-1:1 ratio… e.g., humans having a bias in favor of heads or tails.
Or, if each person is laying a coin down next to the previous coin, such that they are able to see the pattern thus far, we might find any number of pattern-level biases… e.g., if told to simulate randomness, humans might be less than 50% likely to select heads if they see a series of heads-up coins, whereas if not told to do so, they might be more than 50%.
It’s kind of an interesting question, actually. I know there’s been some work on detecting test scores by looking for artificial-pattern markers in the distribution of numbers, but I don’t know if anyone’s done equivalent things for coinflips.
Thank you. I realized, as soon as I posted it, that the method of obtaining the sequence would not matter (as the previous commenter rightly said), but somehow, the ‘feeling of a question’ remained. I was not thinking of showing them part of ‘the sequence so far’… But it might be fun to determine whether there is any effect on the subject’s choice of knowing this ‘flip’ is a part of a pattern (or not knowing it), of the composition of the revealed pattern, and maybe—if there is an effect—the length of the washout period...
I mean, it’s only a coin flip! The preceding choices should have no bearing on it. It’s, like, the least significant choice you can ever make...