Understanding PSD: Why Does Power at High Frequencies Affect Low Frequencies? I'm trying to wrap my head around power spectral density on a conceptual level, but I am having some difficulty. Suppose I have a communication system where I am receiving and sampling white Gaussian noise, which has a uniform PSD. Increasing the bandwidth of my receiver results in greater noise power across all frequency components. I can follow the math of how this occurs: integrate the PSD across more frequencies and you will increase the total power, which is uniform for white noise. However, how does it conceptually make sense that letting in higher frequency power components directly increases the power of the low frequency components?

EDIT: Some commentators have pointed out that my question was worded poorly. I apologize. I will try to clarify what I mean by turning it into a concrete thought experiment. Suppose I have a LPF with bandwidth $B_1$. Then, the variance of the DC component in the DFT domain after sampling WGN will be some $\sigma_1^2$. Now, compare that with a wider LPF with bandwidth $B_2$, where $B_2 &gt; B_1$. Then the DC component variance would be greater, $\sigma_2^2  &gt; \sigma_1^2$. Now, what if instead I implemented this second LPF filter a different way, with a parallelized structure: the sum of 2 different filter paths. The first filter path is the LPF with bandwidth $B_1$ and the second is a bandpass filter with passband $[B_1, B_2]$. This is followed by sampling. This is an equivalent structure to the LPF with bandwidth $B_2$, so the output statistics should be the same. However, notice that the second path is DC blocking, so it will never have a DC component. This should mean that the variance of the DC of the sum of the paths is equal to just the variance of LPF path, $\sigma_1^2$. But clearly, despite this being an equivalent filter structure, the output statistics are not the same. Why?
The spectrum allows you giving another complementary look on the data.
It is the other side of the same coin.

In Frequency Domain we are looking on the energy of each spectral component of our data.
Each component is independent of other component in the meaning each component is orthogonal to any other component.

In the case of White Noise, the PSD is the Fourier transform of the Auto Correlation function.
The model is Continuous White Noise is one which has infinite power.
Its power becomes finite once it is sampled by a sampling chain which has Low Pass Filter (Which the component which limits the incoming power) and AD.  

Now, just like LPF will limit the power of any signal which has bandwidth which is higher the LPF bandwidth it does so to White Noise as well.

Namely, the Constant Level of the PSD is a property of the White Noise alone (In case of Gaussian White Noise it is the $ \sigma $ parameter) while the power of the sampled noise is derived by the level of the PSD and its bandwidth (Which is a property of the LPF).

There is no high frequencies which changes the values of low frequencies.
The power gets bigger because of the hidden assumption that when you sample with higher rate you use LPF with higher bandwidth which lets more frequencies in and hence more power.