Let $V = F_2^n$ be the $n$-dimensional vector space over the field of two elements. The $\epsilon$-noise distribution on $V$, denoted $\mu_\epsilon$, is a probability distribution on $V$ for which sampling a vector $w$ is done by setting each coordinate of $w$ independently, and a particular coordinate is set to $1$ with probability $\epsilon$ and to $0$ with probability $1-\epsilon$.
Let $C \subseteq V$ be a linear subspace, and let $e \in V \setminus C$ be a vector not in $C$. Consider the following pair of events concerning a vector $x$ drawn from $\mu_\epsilon$:
$E_1$ is the event that $x$ is in $C$
$E_2$ is the event that $x$ is in the coset $C + e$
Some experimentation (picking some choices for $C$ at random and also some popular linear codes) suggests that, when $\epsilon < 1/2$, the probability of $E_1$ occurring is at least the probability that $E_2$ occurs, for any $C$. Does anyone have any solid intuition or a proof for why this might be true? Or know of an example where it is false?