> That number is most certainly not random, there are biases in it, you just don't know there are. Indeed. In that specific example, the author generated 24 bi

> That number is most certainly not random, there are biases in it, you just don't know there are. Indeed. In that specific example, the author generated 24 binary digits, but only on 7 occasions was the digit the same as the previous digit. There were 23 opportunities to do so, and in a truly random sequence of digits the chance of any digit being the same as the previous one is 50%. Applying the binomial distribution, the probability of observing 7 or fewer repeats with 23 trials and a 50% chance on each trial is only 4%. So with p = 0.04, we can reject the null hypothesis that the author's generated sequence is random, and conclude that it is biased. There are online demos where you can try this yourself: https://people.ischool.berkeley.edu/~nick/aaronson-oracle/index.html https://people.ischool.berkeley.edu/~nick/aaronson-oracle/in...