Thanks for the link Rick, the explanation there is gold. If no one minds too much, I'll quote the last paragraph because it states trade off for the mean vs median combine methods very clearly.
Quote:
By comparing equations 10 and [6], we see that the SNR achieved by a median combination is approximately a 20% less than the SNR of the average combination of the same images (even less for small sets of images [16]). In terms of SNR improvement, average combination is always better, so what can a median combination be useful for? The answer leads to the subject of robust estimation. For a distribution with a strong central tendency, the median is a robust estimator of the central value. This makes median combination an efficient method for image combination with implicit rejection of outliers, or pixels with too low or too high values due to spurious data. However, in our implementation we provide several pixel rejection algorithms that achieve similar outlier rejection efficiency and can be used with average combination without sacrificing so much signal.

My misunderstanding was of the limits of standard deviation of median not approaching the SD of the mean better than 80%  which only goes to show I didn't understand the maths properly because I knew this once but had forgotten it.
Cheers,
Cam