Are resolution increase and noise reduction from oversampling mutually exclusive? Oversampling a signal means sampling it with a significantly higher sampling frequency than the Nyquist rate. As far as I know, there are three advantages:


Easier design of anti alias filter
Increase in resolution in the presence of a white noise signal (I was told to assume the quantization error can under the right conditions be taken as this noise). While the uncorrelated noise adds up destructively (amplitude raises by $\sqrt N$), the signals amplitude increases by a factor of $N$. This means the SNR increases by $\sqrt N$, acting as an increase in resolution.
Decrease in noise. The quantization noise power is independent of the sample rate and only depends on the amount of bits. So the same noise power is distributed over a larger frequency band, resulting in less noise power per frequency. The a low pass filter can be used to remove the high frequency part of the signal.


If I average over multiple values I am back to the lower sample rate I started with, so I don't think the noise would decrease as described in 3. This leads me to think 2. and 3. are exclusive.
My question is, are 2. and 3. exclusive or can you benefit from both when oversampling?
If you just average multiple samples, you are using a very low quality anti-aliasing low-pass filter which can add a ton of noise. If you then re-quantize after averaging, you add still more noise.  

If you don't (re)quantize after a high quality anti-aliasing low-pass filter (but keep a ton more bit of resolution), then noise could still decrease as per (3).  And if you FFT before your resampling to a lower sample rate, you still get the higher bin resolution of a longer FFT.  So (2) and (3) need not be mutually exclusive.