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In statistics, the Fisher transformation of a Pearson correlation coefficient is its inverse hyperbolic tangent. When the sample correlation coefficients r is significant , its distribution is highly skewed, which makes it difficult to estimate confidence intervals and apply tests of significance for the population correlation coefficient ρ. The Fisher transformation solves this problem by yielding a variable whose distribution is approximately normally distributed, with a variance that is stable over different values of r.
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