Why correlation does not imply causation, with confounding variable and spurious correlation examples drawn from social research.
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- What is a correlation?
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A statistical relationship where two variables move together.- What is causation?
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One variable directly produces changes in another.- Define a confounding variable.
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A third variable that influences both the independent and dependent variables.- What is a spurious correlation?
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An apparent relationship between variables that is not due to a direct causal link.- What is temporal precedence?
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The cause must occur before the effect.- What is covariation in causal inference?
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The cause and effect must be related; changes in one must accompany changes in the other.- What does 'no plausible alternative explanations' mean in causation?
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Other variables cannot provide a simpler or more parsimonious explanation for the relationship.- Explain the 'third variable problem.'
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X and Y correlate because both are influenced by Z, not because X causes Y.- What is reverse causality?
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The presumed direction of causation is backwards; Y may cause X instead of X causing Y.- What is selection bias as a confound?
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The sample studied differs systematically from the population in ways related to both variables.- What is regression to the mean?
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Extreme values tend to move closer to the average on a second measurement, creating spurious correlation.- What is attrition bias?
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Participants systematically drop out, changing sample composition and confounding relationships.- What are demand characteristics?
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Cues that communicate the study hypothesis, causing participants to alter behavior to confirm it.- Why do ice cream sales and drowning deaths correlate?
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Both increase during summer months; warm weather is the confounding variable.- What confound explains the education-income correlation in social research?
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Family socioeconomic status influences both education attainment and earning potential.
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