Similarly, the equivalence of different versions of the same measure can be indexed by a Pearson correlation, and is called equivalent forms reliability or a similar term.

Other correlation coefficients have been developed to be more robust than the Pearson correlation - that is, more sensitive to nonlinear relationships.

The Pearson correlation is defined only if both of the standard deviations are finite and both of them are nonzero.

Note that the examples are sometimes said to demonstrate that the Pearson correlation assumes that the data follow a normal distribution, but this is not correct.

Multiple mechanisms such as Pearson correlation and vector cosine based similarity are used for this.

Pearson correlations were computed to evaluate the relationship between specific continuous variables.

This could either use the deviation score for each person from the group or expert mean; or a Pearson correlation between their judgments and the group mean.

In that study, the CD technique, making use of Pearson correlations accurately predicted the correct number of factors 87.14% of the time.

The Pearson correlation can be expressed in terms of uncentered moments.

An important property of the Pearson correlation is that it is invariant to application of separate linear transformations to the two variables being compared.