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Statistical noise


Statistical noise is unexplained variability within a data sample. The word "noise" has its roots in telecom signal processing; in that context, noise describes unexplained electrical or electromagnetic energy that can degrade the quality of signals and corresponding data.
In both telecom and data science, the presence of noise can significantly affect sampling. Sampling is an analysis technique in which a representative subset of data points is selected, manipulated and analyzed to identify signals, which are patterns in a larger data set. Signals are important because they are the patterns the analyst needs to examine in order to draw conclusions.

Noise can interfere with signals, however, and cause the analyst's attention to be misdirected. A popular solution is to use algorithms that can help separate noise from signals, but even this can be problematic.

In machine learning (ML), for example, statistical noise can create problems when algorithms are not trained properly. This can be dangerous in ubiquitous computing, because if an algorithm classifies noise as a pattern, it will use that pattern to start making generalizations and extrapolations. 

The terms statistical noise and statistical bias are sometimes confused. While both concepts deal with overestimating or underestimating the importance of a variability, a bias can be reproduced reliably, while noise cannot.

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