Linked from

The 26 pages that link to Correlation, each with the reason it gives.

• Causal inferenceCompared with: Correlation alone does not establish that changing one variable changes the other.

• FeedbackCompared with: A correlation alone does not establish the causal return path required for feedback.

• CausalityCompared with: Correlation alone does not establish that either variable causes the other.

• Sensitivity analysisCompared with: Correlation alone does not establish how changing an input changes a model output.

• ConfoundingCompared with: Correlation describes association but does not establish whether confounding produced it.

• Principal component analysisRelated: PCA replaces correlated input variables with component scores that are uncorrelated.

• Francis GaltonBroader topic: Galton introduced the term and developed ways to quantify resemblance between related measurements.

• Independence (probability theory)Compared with: Zero correlation can hold without independence, except under special distributional conditions.

• Regression analysisRelated: Correlation describes pairwise association but does not specify a regression model.

• CovarianceCompared with: Unlike covariance, correlation is unitless and bounded between −1 and 1.

• RatioCompared with: Correlation concerns co-variation, not the quotient of one quantity by another.

• Karl PearsonNarrower topic: Pearson’s coefficient is a specific numerical measure within the broader idea of correlation.

• Scientific explanationCompared with: Association alone cannot establish the causal connection many explanations propose.

• Scatter plotRelated: A scatter plot makes positive, negative, and weak correlations visible.

• Data typesRelated: A correlation coefficient encodes numerical relationships and is not automatically suitable for every scale.

• DiversificationRelated: Lower return correlation between holdings generally makes their combined portfolio less volatile.

• Modern portfolio theoryRelated: Correlations make the diversification effect of asset co-movement easier to compare.

• MulticollinearityNarrower topic: Pairwise correlation is related to, but cannot fully detect, dependence involving several predictors.

• Harry MarkowitzRelated: Correlation makes the diversification effect between asset returns easier to interpret.

• John Stewart BellNarrower topic: The strength of correlations, rather than individual outcomes, is what Bell’s tests compare.

• Mean–variance analysisRelated: It helps explain diversification by expressing how asset returns co-move.

• Line chartRelated: Multiple plotted lines can suggest co-movement, but visual alignment alone does not establish causation.

• Regression toward the meanRelated: When repeated measurements are imperfectly correlated, extreme values tend to be followed by less extreme ones.

• Condorcet's jury theoremCompared with: Correlated errors can prevent added voters from providing the independent information the theorem needs.

• Correlation does not imply causationNarrower topic: Correlation quantifies association, but does not identify its causal source.

• Positive economicsRelated: Positive claims must avoid treating an observed economic association as proof of a causal mechanism.