I thought that the following would made a nice exercise, but I am not sure how to evaluate its difficulty since I often miss elementary solutions. How about you try answering it? It would be great to have several proofs.
Let $m$ be a positive integer and $p \in (0,1)$. Prove that $\displaystyle \lim_{n\to\infty}\underset{m\mid k}{\sum_{0 \leq k \leq n}} \binom{n}{k}p^{k}(1-p)^{n-k} = \frac{1}{m}$.
I give two proofs of this limit below, but I am still interested in elementary proofs.
Edit 1. As noted by @rlgordonma, the sum is $\lim_{n \rightarrow \infty} \sum_{\ell = 0}^{\lfloor \frac{n}{m} \rfloor} \binom{n}{\ell m} p^{\ell m} (1-p)^{n - \ell m} = \frac{1}{m}$. Maybe it makes things clearer like that.
Edit2. Thank you for your participation. I will now give two proofs of this limit (one of them being a reformulation of vonbrand's solution). In fact, I will prove a little more:
$$\forall r \in \{0,\dots,m-1\},\qquad\lim_{n\to\infty} \sum_{\substack{0 \leq k \leq n\\ k \equiv r\; [m]}} \binom{n}{k}p^k(1-p)^k = \frac{1}{m}$$
Step 1 (probabilistic interpretation).
This step is not necessary, but I think it brings a better insight. Let $X_1,X_2,\dots$ be a sequence of independent Bernoulli random variables with parameter $0 < p <1$, that is $P(X_i=1)=1-P(X_i=0)=p$.
Then $S_n = X_1 + \dots+ X_n$ is a Binomial random variable with parameters $(n,p)$, and the sum we are interested in is just: $$\sum_{\substack{0 \leq k \leq n\\ k \equiv r\; [m]}} \binom{n}{k}p^k(1-p)^k = P(S_n \equiv r\;[m]).$$ The sequence $(S_n)$ is a random walk over $\mathbb{Z}$ but since we are only interested in its residue modulo $m$ it will be better to see it as a random walk over $\mathbb{Z}/m\mathbb{Z}$ (with an abuse of notation, we will still write $S_n$ for this residue).
Step 2 (limit behaviour of the random walk). Quick version, with Markov Chains
The random walk $(S_n)$ is an irreducible (and aperiodic) Markov Chain over the finite state space $\mathbb{Z}/m\mathbb{Z}$ with transition matrix given by $Q(i,i)=1-p$ and $Q(i,i+1)=p$ for $i \in \mathbb{Z}/m\mathbb{Z}$. It admits the uniform distribution as a stationnary distribution, hence it converges to this distribution. QED
Step 2 (bis). Without Markov Chains
The following result (inversion formula for the discrete Fourier transform) is a generalization of @vonbrand's trick:
Note $S = \{0,\dots,m-1\}$ and $\omega=\exp\left(i2\pi/m\right)$. For every function $f \colon S\to \mathbb{C}$ and every $x \in S$, $$ f(x) = \sum_{k = 0}^{m-1} \widehat{f}(k)\,\omega^{kx},\qquad\text{where}\quad \widehat{f}(k) = \frac{1}{m}\sum_{x=0}^{m-1}f(x)\omega^{-kx}. $$
In particular, we have $$ E(f(S_n)) = \sum_{k=0}^{m-1}\widehat{f}(k)\,E\left[\omega^{k S_n}\right] $$ The sequence $X_1,X_2,\dots$ being i.i.d., $$ E\left[\omega^{k S_n}\right] = \left(E\left[\omega^{k X_1}\right] \right)^n = ((1-p)+p\omega^k)^n \xrightarrow[n\to\infty]{}\begin{cases}1 & \text{if } k=0\\0 & \text{else}\end{cases} $$ Hence, $\lim_{n\to\infty} E[f(S_n)] = \widehat{f}(0)$. Taking $f(x) = 1_{\{x = r\}}$ yields the result.
