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MENTAL MODEL · M11193

Correlation Analysis

Correlation Analysis
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Updated 2026-08-13

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INTRODUCTION

English translation pending.

CORE DEFINITION

Correlation analysis quantifies the degree to which two variables move together linearly, summarized by a correlation coefficient ranging from minus one to plus one, and often displayed as a matrix of pairwise coefficients. The core proposition is that it identifies relationships worth investigating, not causal claims. The qualification is that a coefficient detects only linear association, so strong nonlinear relationships can register as zero, and a high value can result from a common cause rather than from any direct influence.

SCAFFOLDING EFFECT

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- Association scan: compute pairwise coefficients to see which variables move together. - Significance filter: check whether the observed association exceeds what sampling noise would produce. - Causation guard: treat every coefficient as a candidate hypothesis rather than a finding.

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The coefficient measures how much of the joint variation between two variables is shared, which is symmetric and directionless: it cannot distinguish a cause from an effect or from a common driver. Because it is a summary of linear covariance, it responds to co-movement regardless of its origin, so the measure identifies candidates for further study rather than the structure that generated the data.

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    en.wikipedia.orghttps://en.wikipedia.org/wiki/CorrelationZH · Explicit
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