Signal to Noise Ratio

The Signal to Noise Ratio (SNR) is used to define the level of signal compared to the level of noise Signal to noise ratio.  The ratio takes the power of the signal divided by the power of the noise, with a ratio of greater than 1:1 indicating more signal than noise.  While the SNR was originally used in the Shannon-Hartley theorem to calculate the maximum transmission of data in the presence of noise, it can also be a very useful construct trying to understand current events.

For example, every day there is a tremendous amount of "signal" being generated or promulgated through the media.  But the question is always, is this really a signal or noise.  One particularly prominent "signal" is the monthly employment report from the Bureau of Labor Statistics.  Each month a report is released with a number of jobs being created and a corresponding unemployment rate.  These reports are nearly always treated as if they are clean signals with high quality information.  Other "signals" could be recent events from overseas, scandals in the White House, and on and on.

One approach to each signal is use the criteria from my previous post and measure each signal against the three heuristics; common sense, simplicity, and who benefits?  With these in mind, most so-called "signals" can immediately be seen as noise and as such reduce the amount of real signals in the environment.  The trick then becomes filtering out all of the noise masquerading as signal and concentrating on finding and evaluating the signals.