I have a question about the connection between continuous time white noise and discrete white noise (i.e. i.i.d gaussians). If I understand correctly you cannot derive a discrete white noise process from a continuous one, since the derivative of a Brownian motion (i.e continuous white noise) is too "rough".
Consider the following: Assume $X$ to be continuous time white noise and $f$ square Riemann integrable with $\int_0^1 f(t)^2 dt = 1$ then \begin{equation} \mathbb{E}\left[\left(\int_0^1 X(t) f(t) dt \right)^2 \right] = \int_0^1 f(t)^2 dt = 1. \end{equation}
On the other in the discrete case ($\epsilon_t$ i.i.d gaussian): \begin{equation} \mathbb{E}\left[\left(\frac{1}{n} \sum_{t=1}^n f(t/n) \epsilon_t \right)^2\right] = \frac{1}{n^2} \sum_{i=1}^n f(t/n)^2 \to 0. \end{equation}
I know this is mathematical quite imprecise. However, I wanted to know if I am misunderstanding something or if these quantities actually don't match up. If they don't: Is there an intuition why this is not the case?