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Spurious Correlation
Spurious correlation is when two variables appear to be directly influencing each other when in reality they are not. Usually this misconception is caused by a confounding variable. DAGs (directed acyclic graphs) can help visualize which variables may influence each other and indicate if there is a possibility for a spurious correlation.
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Bayesian Statistics
Statistics
Data Science
KPU
Research Methods in Psychology - 4th American Edition @ KPU
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Directed Acyclic Graph (DAG)
Example of a Spurious Correlation: Chocolate and Nobel Prizes
What term describes a situation where two variables appear to be directly influencing each other, but in reality, their relationship is an illusion usually caused by a confounding variable?
A researcher discovers a strong positive correlation between the number of hours students spend on social media and their reported levels of anxiety. However, further analysis reveals that a third variable—academic workload—independently increases both social media use (as a coping mechanism) and anxiety levels. In this scenario, the original correlation between social media use and anxiety is best described as a spurious correlation.