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Strictly defined, statistical noise is a term that refers to the unexplained variation or randomness that is found within a given data sample or formula. There are two primary forms of it: errors and residuals. 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. A residual is the result of a more casual estimate of the anticipated outcome. The general notion behind statistical noise is that a particular set of data is not necessarily precise and might not be able to be duplicated if the same information was collected or calculated again.
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. Although generating 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 statistical noise must be taken into account.
A cloth manufacturer, for example, might develop production statistics related to the amount of cloth that can be produced within one hour. There are several factors that can affect the average amount of cloth produced, such as the quality of the base product, machinery malfunctions, operator error and even the temperature and humidity level on the plant floor. Statistical noise would be taken into account by removing the effects of those elements that are simply not likely to occur during the course of a typical shift, because including them would not result in a true picture of average production.
Many people assume that statistical errors and residuals are two references for the same occurrence, but in fact, they are different aspects. Generally, there has been some calculation involved with statistical errors and some degree of effort applied to the task. An error can cause the final total to be higher or lower. 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 no calculation involved.
Statistical noise is not something that should be considered worthless. In the field of business, the questions that are raised by statistical noise often point to situations that, while not common during the average workday, are still capable of occurring and derailing production for an extended period of time. From this perspective, the noise can be the inspiration for creating and implementing safeguards that help to maintain steady and predictable operations.
@Logicfest -- But that so called statistical noise can tell us a lot in social sciences. Let's say, for example, that a survey is created by a political scientist based on the assumption that people will vote according to what is best for them.
The survey is given, the results are analyzed and it is discovered that people tend to rigidly stick with a political party rather than voting in their own, self interest.
Is that statistical noise or something about human nature that can teach us quite a bit? I'd wager the latter part of that is true.
And this is why statistics are so difficult to use effectively in social sciences. If you are talking about the behavior of people, you are dealing in an area where unexplained, random behavior is often normal.
Dealing with the likelihood of machines breaking down or running out of material is one thing. Dealing with the strange things that people do is quite another.
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