Overview
Correlation analysis is the most misinterpreted statistical result in practice. Significant correlations are routinely described as causal relationships — "X causes Y" — when the test only establishes that two variables co-vary. Correlations can reflect a causal relationship, a shared cause (third variable), a reversed direction, or coincidence. The analysis must report what the correlation establishes (co-variation) and explicitly state what it does not establish (causation), then address what additional evidence would be needed to make causal claims.
The Correlation Analysis Framework applies the correct correlation coefficient for the data type, reports both magnitude and direction, visualizes the relationship to check for non-linearity and outlier influence, and produces interpretations that accurately characterize the relationship without overclaiming causation.