Signal vs. noise at a particular frequency I collect samples (time series) at a frequency.  The sample rate is arbitrary (I can change it to whatever I want.)  I want to determine if what's at this frequency is a signal, or (normally distributed) noise.

I've tried using a normality test (from scipy); that didn't seem to work - there were no big differences in the results when what was at the frequency was a man-made signal (center frequency of an FM broadcast station, for example) vs. noise.  I tried using auto-correlation using Pandas - Pandas always thought it was noise.

Are my techniques off?  Perhaps the techniques are good, but I need to sample different (I've been sampling about every two seconds, for, say, 100 samples)?  Something else?
The usual scenario is where you want to find if the received signal is noise: $$r(t)=n(t),$$ or if it is a signal plus noise: $$r(t)=s(t)+n(t).$$ It is generally assumed that you know $s(t)$. For example, you could have a radar application where you transmit a pulse and need to find if it was reflected back; or a digital communications receiver, where you want to see if the signal received was $p(t)$ (say, indicating a bit 0) or $q(t)$ (a bit 1).

The way to do this is by calculating the correlation between $r(t)$ and $s(t)$. The idea is that if the signal is present, you have
\begin{align}
\text{corr}(r(t),s(t)) &amp;= \text{corr}(s(t)+n(t),s(t))\\
&amp;=\text{corr}(s(t),s(t))+\text{corr}(s(t),n(t))
\end{align}
which is large. If the signal is not present, then the correlation is small.

This scheme also works in the digital domain, assuming that you sample fast enough. In general you want to calculate the correlation of a large number of samples, so that (by the law of large numbers) the probability of being unlucky and getting "bad" noise samples is small.

In your question, you don't say if you know what the signal is. If you don't, but you know some of its properties, you may be able to look for it. For instance, if the noise is zero mean and the signal is not; or if you know that the signal's energy is concentrated in a certain band (the noise is flat). However, a specific answer is impossible without more information.

If you don't know anything about the signal, detecting it may well be impossible.