Statistical distance between uniform and biased coin
Denote the random bits by $x_1,\dots, x_n$. By definition, the statistical distance between $U$ and $D$ is at least $\Pr_U\left(\sum x_i \geq t\right) - \Pr_D\left(\sum x_i \geq t\right)$ for every $t$. We choose $t = n/2 + \sqrt{n}$. 

Note that $\Pr_U\left(\sum x_i \geq t\right) \geq c_1$ for some absolute constant $c_1 &gt; 0$. If $\Pr_D\left(\sum x_i \geq t\right) \leq c_1/2$, then the statistical distance is at least $c_1/2$, and we are done. So we assume below that $\Pr_D\left(\sum x_i \geq t\right) \geq c_1/2$.

Let $f(s) = \Pr\left(\sum x_i \geq t\right)$ for i.i.d. Bernoulli random variables $x_1,\dots, x_n$ with $\Pr(x_i = 1) = 1/2-s$. Our goal is to prove that $f(0) - f(\varepsilon) = \Omega(\varepsilon \sqrt{n})$. By the mean value theorem, 
$$f(0) - f(\varepsilon) = -\varepsilon f'(\xi),$$
for some $\xi \in (0, \varepsilon)$. Now, we will prove that $-f'(\xi) \geq \Omega(\sqrt{n})$; that will imply that the desired statistical distance is at least $\Omega(\sqrt{n} \varepsilon)$, as required. 

Write,
$$f(\xi) = \sum_{k\geq t} \binom{n}{k} \left(\frac12 - \xi\right)^k \left(\frac12+\xi\right)^{n-k},$$
and
$$\begin{align}
f'(\xi) &amp;= \sum_{k\geq t} \binom{n}{k} \left(-k \left(\frac12 - \xi\right)^{k-1} \left(\frac12+\xi\right)^{n-k} + (n-k) \left(\frac12 - \xi\right)^{k} \left(\frac12+\xi\right)^{n-k-1}\right) \\
&amp;= -\sum_{k\geq t} \binom{n}{k} \left(\frac12 - \xi\right)^{k} \left(\frac12+\xi\right)^{n-k}\frac{k/2 + k\xi - (n-k)/2 + (n-k)\xi}{(1/2 - \xi)(1/2 +\xi)}.
\end{align}$$
Note that 
$$\frac{k/2 + k\xi - (n-k)/2 + (n-k)\xi}{\left(1/2 - \xi\right)\left(1/2 +\xi\right)} = 
\frac{(2k-n)/2 + n\xi}{(1/2 - \xi)(1/2 +\xi)} \geq 2(2t - n)  = 4\sqrt{n}.$$
Thus,
$$\begin{align}-f'(\xi) &amp;\geq 4\sqrt{n} \sum_{k\geq t} \binom{n}{k} \left(\frac12 - \xi\right)^{k} \left(\frac12+\xi\right)^{n-k} \\&amp;= 4\sqrt{n} f(\xi) \geq 4\sqrt{n} f(\varepsilon) \geq 4\sqrt{n}\cdot (c_1/2).\end{align}$$
Here, we used the assumption that $f(\varepsilon) = \Pr_D(x_1+\dots+x_n \geq t) \geq c_1/2$. We showed that $-f'(\xi) = \Omega(\sqrt{n})$.