My instructor proved the central limit theorem using the characteristic function. I think the proof is a standard one because I found basically the same proof in wikipedia.
So for i.i.d. ${X_1, X_2,\cdots, X_n}$ with $E[X_i]=\mu$ and $\text{Var}(X_i)=\sigma^2<+\infty$, he then defined $$Y_i=\frac{X_i-\mu}{\sqrt{n}}$$
Then $E[Y_i]=0$ and $\text{Var}(Y_i)=\sigma^2/n$.
So the characteristic function of $Y_i$ is $$\phi(k)=1-\frac{k^2\sigma^2}{2n}+o\left(\frac{1}{n}\right)$$
Then he further define $$Z=Y_1+Y_2+\cdots+Y_n$$ and so the characteristic function of $Z$ is $$\Phi(k)=\phi(k)^n=\left(1-\frac{k^2\sigma^2}{2n}+o\left(\frac{1}{n}\right)\right)^n$$
Then he claimed that when $n\rightarrow\infty$ $$\Phi(k)=\lim_{n\rightarrow\infty}\left(1-\frac{k^2\sigma^2}{2n}+o\left(\frac{1}{n}\right)\right)^n=\exp\left(-\frac{k^2\sigma^2}{2}\right)$$
Performing the inverse Fourier transform we get $$P(Z)=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{Z^2}{2\sigma^2}\right)$$
Then finally defining $$\tilde{X}=\frac{Z}{\sqrt{n}}+\mu=\frac{X_1+X_2+\cdots+X_n}{n}$$ it can be shown that $$P(\tilde{X})=\frac{1}{\sqrt{2\pi}(\sigma/\sqrt{n})}\exp\left(-\frac{(\tilde{X}-\mu)^2}{2(\sigma/\sqrt{n})^2}\right)$$
I found slightly different proof in wiki but the basic idea is the same.
Now where I have problem is the step $$\lim_{n\rightarrow\infty}\left(1-\frac{k^2\sigma^2}{2n}+o\left(\frac{1}{n}\right)\right)^n=\exp\left(-\frac{k^2\sigma^2}{2}\right)$$
I know that I cannot stupidly put the limit inside the bracket for otherwise I'd get the answer $1^n=1$. But why do I know I cannot do it to the $O(1/n)$ term, but can do it to the $o(1/n)$ terms?
Are there something I am missing?