We study the problem of distinguishing between two independent samples $\mathbf {G}_n^1,\mathbf {G}_n^2$
of a binomial random graph $G(n,p)$
by first-order (FO) sentences. Shelah and Spencer proved that, for a constant $\alpha \in (0,1)$
, $G(n,n^{-\alpha })$
obeys FO zero-one law if and only if $\alpha $
is irrational. Therefore, for irrational $\alpha \in (0,1)$
, any fixed FO sentence does not distinguish between $\mathbf {G}_n^1,\mathbf {G}_n^2$
with asymptotical probability 1 (w.h.p.) as $n\to \infty $
. We show that the minimum quantifier depth $\mathbf {k}_{\alpha }$
of an FO sentence $\varphi =\varphi (\mathbf {G}_n^1,\mathbf {G}_n^2)$
distinguishing between $\mathbf {G}_n^1,\mathbf {G}_n^2$
depends on how closely $\alpha $
can be approximated by rationals:
The main ingredients in our proofs are a novel randomized algorithm that generates asymmetric strictly balanced graphs as well as a new method to study symmetry groups of randomly perturbed graphs.