Neutral vector, a multivariate random variable that exhibits a particular type of statistical independence (Dirichlet distribution).

Although the random values of a stochastic process at different times may be statistical independence, in most commonly considered situations they exhibit complicated statistical correlations.

It is often made with the stronger condition that the variables are statistical independence, but uncorrelatedness suffices.

If 'X' and 'Y' are statistical independence, then their covariance is zero.

When is diagonal the standard representation can be shown to have zero correlation but the marginal distributions do not agree with statistical independence.

If the number of runs is significantly higher or lower than expected, the hypothesis of statistical independence of the elements may be rejected.

Kac became interested in the occurrence of statistical independence without randomness.

This last equation is in the form of a Markov-type statistical independence.

Once proper adjustments are made that deal with external dependencies, then the axioms of probability theory concerning statistical independence will apply.

Critics of Meadow's law state that it is based on a fundamental misunderstanding of statistics, particularly relating to probability, likelihood, and statistical independence.