White noise is a random process whose "components each have a probability distribution with zero mean and finite variance, and are statistically independent".

White noise is a random process whose "components each have a probability distribution with zero mean and finite variance, and are statistically independent" (Wikipedia). When white noise is distributed like a normal distribution it is called Gaussian white noise.

In statistics and econometrics one often assumes that an observed series of data values is the sum of a series of values generated by a deterministic linear process, depending on certain independent (explanatory) variables, and on a series of random noise values. Then regression analysis is used to infer the parameters of the model process from the observed data, e.g. by ordinary least squares, and to test the null hypothesis that each of the parameters is zero against the alternative hypothesis that it is non-zero.