Filtering on

Noise: The aspects of observations or stimuli that distract from, dilute, or get confused with signal, and are not signal (i.e., do not provide useful information about the target of interest).   

Observations/stimuli subject to confusion between signal and noise include communication, measurements, descriptions, etc.   

Signal-to-Noise Ratio: The relative strength of signal compared to the relative strength of noise in a given context. Obtaining meaningful information from the world requires distinguishing signal from noise. Therefore, human cognition (both scientific and otherwise) relies on techniques and tools to suppress noise and/or amplify signal (i.e., increase signal-to-noise ratio).    

It is possible to design filters to increase the signal-to-noise ratio, if you know where the noise is going to appear.  

People are (evolutionarily?) disposed to over-perceive signal (i.e., noise often gets misinterpreted as signal), perhaps because the cost of missing real signal (false negatives) is typically higher than the cost of mistaking noise for signal (false positives).   

People tend to see any regularity as a pattern (i.e., see more signal than there is), even when “patterns” occur by chance (i.e. are pure noise), e.g.: People underestimate the frequency of apparent patterns produced by randomness, leading to overperception of spurious signal much more frequently than people account for.  (Events that are just coincidental are much more likely than most people expect.)  

Good decision-making under uncertainty involves having sufficient signal (an adequate test) and setting your threshold appropriately for the relative costs of false positives and false negatives.     

In some classification cases like pornography identification or graduate school admissions, there may not be a “truth of the matter” so there aren’t true “false” positives or “true” negatives, although a threshold must still be set.