Basically, any series of measurements can be thought of as being composed of two parts -- the "signal" (which is the value we're actually trying to measure) and the "noise" (which is made of all the things that we're not really interested in, but which affect the data anyway).
We generally can't figure out exactly what is signal and what is noise. but if we make some assumptions about what is producing the noise, we can make a reasonable guess as to what percentage of the overall information in the data is due to signal and what percentage is due to noise. That is what the S/N ratio is. A S/N ratio of 1.0 means that we estimate that the noise is having just as much affect on the data as the signal is. Clearly, that makes it pretty hard to interpret that data. Ideally, all the S/N ratios would be very large, so large we could say that the data represented "truth", but that is often not the case. So the S/N ratio for a synthesized design just gives us a rough idea of how much weight we can put on it when using it in support of a particular theory. (Omei)