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Also known as: Statistical Dependence
A statistical relationship in which variables are not independent, indicating association or predictive linkage.
DESCRIPTION
Dependence captures relationships between variables beyond chance. It includes linear correlation, nonlinear association, and conditional dependencies revealed by probability models. Mutual information measures general dependency; covariance focuses on linear relationships. Distinguishing dependence from causation is critical in analysis. In graphical models, edges encode dependencies and d-separation tests conditional independence. Recognizing dependence informs feature selection, modeling strategy, and bias detection. Proper testing and visualization help avoid spurious associations.
ALTERNATIVE DEFINITIONS
Wikipedia
In statistics, dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data.
Broadly defines dependence including non-causal relationships.
Statlect
Two random variables are dependent if the occurrence of one affects the probability distribution of the other.
Defines dependence via changes in distributions.
Khan Academy
Dependent events are those where the occurrence of one event affects the probability of the other.
Pedagogical focus on events and probabilities.
RELATED TERMS
Correlation Causation Conditional Independence Mutual Information Covariance Semantic Relationship Confounding d-separation Bayesian Network
COMPETING TERMS
RELATIONS
- Narrower: Partial Dependence
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist Information Analyst AI Engineer Data Management Consultant
EXTERNAL SOURCES
- Wikipedia - Correlation and dependence
- Statlect - Correlation and dependence fundamentals
- Khan Academy - Dependent vs independent events
- Carnegie Mellon University - Graphical models and dependencies
TOP QUESTIONS
How do you test for Dependence between variables?
Use statistical tests and measures like Pearson correlation, mutual information, or permutation tests, depending on data type and relationship.
Category: Testing
What’s the difference between Dependence and Causation?
Dependence indicates association; causation means one variable influences another. Causal inference requires stronger assumptions or experimental design.
Category: Misunderstanding
How can visualization reveal Dependence?
Scatter plots, heatmaps, and partial dependence plots can show relationships or conditional effects.
Category: Example
What are risks in misinterpreting Dependence?
Spurious correlations and confounding can mislead decisions; controls and domain knowledge are essential.
Category: Risks
How does Dependence affect feature engineering?
Highly dependent features may be redundant; consider decorrelation or selection to improve model performance.
Category: Management
Is zero correlation proof of independence?
No, zero linear correlation does not rule out nonlinear dependence; use other measures.
Category: Misunderstanding
What models exploit conditional Dependence?
Bayesian networks and Markov random fields encode dependencies for inference and learning.
Category: Overview
What is a hot topic about Dependence measurement?
Nonparametric measures and kernel methods to capture complex associations without strong assumptions.
Category: Topic
How do you control for confounding Dependence?
Use stratification, matching, regression adjustment, or causal graphs to isolate relationships.
Category: Best practices