Many businesses depend a great deal on statistics. Statistical information is used to identify customer preferences and purchasing habits, production costs, and the efficiency of operating structures. While the generation of statistics is a great way to gain a better understanding of how a business should run and the direction it should take, the process can also create some worthless data. This is where the concept of statistical noise comes into being.

Strictly defined, statistical noise is a slang term for the acknowledged variation that is found within a given data sample or formula. For example, within the context of developing production statistics related to the number of yards of cloth that can be produced within one hour, there are a number of factors that can impact the average number of yards produced.

Quality of the base product, machinery malfunctions, operator error, and even the temperature and humidity level on the plant floor can impact the direction that the data will take. Statistical noise would be created by factoring in those elements that are simply not likely to occur during the course of a typical shift, since to do so would not result in a true picture of average production.

There are two forms of statistical noise that are noteworthy. Those two forms are usually referred to as errors and residuals. While determining if a given statistical variation should in fact be considered for the purpose of answering a given question, the concepts of statistical errors and statistical residual come into play. Many people assume they are two references for the same occurrence, but in fact they are different aspects.

A statistical error is simply the portion of the final amount that differs from the expected value that was assumed to be the correct answer. Generally, there has been some calculation involved with this example of statistical noise, and some small degree of effort applied to the task. An error can be higher or lower than the final total.

A residual is simply a more casual estimate of the anticipated outcome. With a statistical residual, there is not a lot of effort made to come up with a logical process. Instead, it is little more than a hunch based on a quick review of the available data, with little or not calculation involved. Examples of residuals are found just about everywhere, and while they may often turn out to be closer to the actual logical outcome, the residual statistical noise is arrived at based on intuition rather than the application of scientific or logical process.

Statistical noise is not something that should be considered completely worthless. Often, the questions that are raised by statistical noise can point to situations that, while not common in the average workday, are still capable of occurring and derailing production for an extended period of time. From this perspective, statistical noise can be the inspiration for creating and implementing safeguards that help to maintain steady and predictable operations.