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((Short description|Data with additional meaningless information in it))
((multiple|
((unreliable sources|date=May 2017))
((sources|date=May 2017))
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'''Noisy data''' are data that is corrupted, distorted, or has a low [[signal-to-noise ratio]].  Improper procedures (or improperly-documented procedures) to subtract out the noise in data can lead to a false sense of accuracy or false conclusions.

Noisy data are data with a large amount of additional meaningless information in it called noise.<ref name="sba">((cite web|url=http://searchbusinessanalytics.techtarget.com/definition/noisy-data|title=What is noisy data? - Definition from WhatIs.com|publisher=))</ref>  This includes [[data corruption]] and the term is often used as a synonym for corrupt data.<ref name="sba" /> It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use un[[structured text]]. Noisy data can adversely affect the results of any data analysis and skew conclusions if not handled properly. Statistical analysis is sometimes used to weed the noise out of noisy data.<ref name="sba" />

== Sources of noise ==
[[File:Contextual Outlier.png|thumb|In this example of an outlier and filtering, point t2 is an outlier.  The smooth transition to and from the outlier is from filtering, and is also not valid data, but more noise. Presenting filtered results (the smoothed transitions) as actual measurements can lead to false conclusions.]]
[[File:Moving Average Types comparison - Simple and Exponential.png|thumb|This type of filter (a [[moving average]]) shifts the data to the right.  The ''moving average price'' at a given time is usually much different than the actual price at that time.]]
Differences in real-world measured data from the true values come about from by multiple factors affecting the measurement.<ref>((cite web|url=http://sci2s.ugr.es/noisydata|title=Noisy Data in Data Mining - Soft Computing and Intelligent Information Systems|website=sci2s.ugr.es))</ref>

[[Random noise]] is often a large component of the noise in data.<ref name=rywang>R.Y. Wang, V.C. Storey, C.P. Firth, A Framework for Analysis of Data Quality Research, IEEE Transactions on Knowledge and Data Engineering 7 (1995) 623-640 doi: 10.1109/69.404034)</ref>  Random noise in a signal is measured as the [[signal-to-noise ratio]].  Random noise contains almost equal amounts of a wide range of frequencies, and is also called ''[[white noise]]'' (as colors of light combine to make ''white'').  Random noise is an unavoidable problem.  It affects the data collection and data preparation processes, where errors commonly occur.  Noise has two main sources: errors introduced by measurement tools and random errors introduced by processing or by experts when the data is gathered.<ref name=xzhu>X. Zhu, X. Wu, Class Noise vs. Attribute Noise: A Quantitative Study, Artificial Intelligence Review 22 (2004) 177-210 doi: 10.1007/s10462-004-0751-8</ref>

Improper filtering can add noise if the filtered signal is treated as if it were a directly measured signal.  As an example, [[Convolution]]-type [[digital filter]]s such a [[moving average]] can have side effects such as lags or truncation of peaks.  Differentiating digital filters ''amplifies'' random noise in the original data.

[[Outlier]] data are data that appear to not belong in the data set.  It can be caused by human error such as transposing numerals, mislabeling, [[programming bugs]], etc.  If actual outliers are not removed from the data set, they corrupt the results to a small or large degree depending on circumstances.  If valid data is identified as an outlier and is mistakenly removed, that also corrupts results.

Fraud:  Individuals may deliberately skew data to influence the results toward a desired conclusion.  Data that looks good with few outliers reflects well on the individual collecting it, and so there may be incentive to remove more data as outliers or make the data look smoother than it is.

==References==
((reflist))

[[Category:Noise]]
[[Category:Digital audio]]

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