Noisy data

Data with additional meaningless information in it Noisy data are data that are corrupted, distorted, or have 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 them, known as noise. This includes data corruption and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use unstructured 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. Sources of noise 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. This type of filter (a moving average) shifts the data to the right. The moving av