Lightweight test for bimodal distributions? Here, and in the stats stackexchange, seem to be answers that reference tests for bimodal distributions that involve iterative binning or iterative curve fitting methods.  However "eyeballing" a plot of a data set often shows a clear bimodality (say a 10 dB dip or several standard deviations  between two clear mode peaks, etc.), versus a single "hump", or something ambiguous (less than a 3 dB dip).

Are there any lightweight algorithms (computationally efficient, single pass, or deterministic low iteration count tests) which can separate out the easy cases (clearly unimodal, or clearly bimodal), versus data sets that may require more computationally intensive tests for likelihood of multi or bi-modality?

Or, in signal processing terms, how can one non-graphically and quickly test whether some area in a noisy spectrogram is more likely to contain 2-tone FSK (with an unknown separation), rather than noise or some other kind of modulated signal?
An easy way, especially one that is used in the detection and SNR estimation of PSK signals in a noisy signal, is based on stochastic moments of the received amplitude:

The kurtosis can be used as measure of how little "centered" a symmetric distribution is.

You could calculate a sample kurtosis frequency bin-wise in your spectrogram; those bins with high curtosis are likely to contain more than just Gaussian noise.

In a slightly more elaborate version of that algorithm optimized for detecting 2-FSK, you could use that to identify "candidate bins", subtract their mean power to have zero mean variables, and then calculate the covariance of pairs of these (i.e. calculate a covariance matrix). You're looking for a strong negative covariance – whenever one bin has above-average power, the other has low power.