"If N = 300, even a 256-bit seed arbitrarily precludes all but an unknown, haphazardly selected, non-random, and infinitesimally small fraction of permissible assignments. This introduces enormous bias into the assignment process and makes total nonsense of the p-value computed by a randomization test."
The first sentence is obviously true, but I'm going to need to see some evidence for "enormous bias" and "total nonsense". Let's leave aside lousy/little/badly-seeded PRNGs. Are there any non-cryptographic examples in which a well-designed PRNG with 256 bits of well-seeded random state produces results different enough from a TRNG to be visible to a user?
What's bullshit about it? This is how TRNGs in security enclaves work. They collect entropy from the environment, and use that to continuously reseed a PRNG, which generates bits.
If you're talking "true" in the philosophical sense, that doesn't exist -- the whole concept of randomness relies on an oracle.
What PRNGs lack compared to TRNGs is security (i.e. preventing someone from being able to use past values to predict future values). It's not that they somehow produce statistically invalid results (e.g. they generate 3s more often than 2s or something). Unless they're very poorly constructed.
I don't think hardware random number generators are bullshit, but it's easy to overstate their importance. Outside of cryptography, there aren't a whole lot of cases that truly require that much care in how random numbers are generated. For the kind of examples the article opens with (web page A/B testing, clinical trials, etc.) you'll never have sample sizes large enough to justify worrying about the difference between a half-decent PRNG and a "true" random number generator.
"If N = 300, even a 256-bit seed arbitrarily precludes all but an unknown, haphazardly selected, non-random, and infinitesimally small fraction of permissible assignments. This introduces enormous bias into the assignment process and makes total nonsense of the p-value computed by a randomization test."
The first sentence is obviously true, but I'm going to need to see some evidence for "enormous bias" and "total nonsense". Let's leave aside lousy/little/badly-seeded PRNGs. Are there any non-cryptographic examples in which a well-designed PRNG with 256 bits of well-seeded random state produces results different enough from a TRNG to be visible to a user?
Starts interesting, then veers into the usual "true random number" bullshit. Use radioactive decay as source of your random numbers!
How do we know it's truly random?
> usual "true random number" bullshit
What's bullshit about it? This is how TRNGs in security enclaves work. They collect entropy from the environment, and use that to continuously reseed a PRNG, which generates bits.
If you're talking "true" in the philosophical sense, that doesn't exist -- the whole concept of randomness relies on an oracle.
What PRNGs lack compared to TRNGs is security (i.e. preventing someone from being able to use past values to predict future values). It's not that they somehow produce statistically invalid results (e.g. they generate 3s more often than 2s or something). Unless they're very poorly constructed.
I don't think hardware random number generators are bullshit, but it's easy to overstate their importance. Outside of cryptography, there aren't a whole lot of cases that truly require that much care in how random numbers are generated. For the kind of examples the article opens with (web page A/B testing, clinical trials, etc.) you'll never have sample sizes large enough to justify worrying about the difference between a half-decent PRNG and a "true" random number generator.