  When testing a set of data for randomness according to a probability
distribution that depends on a parameter, access to this parameter can be
considered as a computational resource. We call a randomness test Hippocratic
if it is not permitted to access this resource. In these terms, we show that
for Bernoulli measures $\mu_p$, $0\le p\le 1$ and the Martin-L\"of randomness
model, Hippocratic randomness of a set of data is the same as ordinary
randomness. The main idea of the proof is to first show that from
Hippocrates-random data one can Turing compute the parameter $p$. However, we
show that there is no single Hippocratic randomness test such that passing the
test implies computing $p$, and in particular there is no universal Hippocratic
randomness test.
