Corrélation

Corrélation, in statistics, refers to a measure that describes the degree to which two variables move in relation to each other. It is a way to quantify the strength and direction of a linear association between variables, and it can also be examined in nonparametric forms for ranks or monotonic relationships.

Common measures include the Pearson correlation coefficient, which assesses linear relationships and ranges from -1 to 1; values near 1 indicate a strong positive linear association, near -1 a strong negative linear association, and around 0 little or no linear association. Rank-based measures such as Spearman's rho and Kendall's tau evaluate monotonic relationships and are less sensitive to outliers or non-normal data. Correlation coefficients are symmetric: Corr(X, Y) equals Corr(Y, X).

Interpreting correlation requires caution. A high or low correlation does not imply causation; two variables may be correlated due to a third confounding factor, coincidence, or other forms of dependence. Correlation can be affected by outliers or by the scale of measurement, and it primarily captures linear or monotonic relationships, potentially missing more complex patterns.

In practice, correlation is often summarized with scatter plots or correlation matrices and is used in exploratory data analysis, feature selection, and data preparation for regression. It is distinct from causality and from broader notions of dependence, and its interpretation should consider study design, underlying theory, and domain context.